Yu Fu

Jianhua Yang

Tingting Zuo

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas *

Keywords: hybrid renewable energy system, loss of power supply probability, genetic algorithm, linearly-weighted fuzzy algorithm

Small-sized hybrid wind-hydro-solar power generation systems may be designed to solve the power supply problem in some rural areas. Optimal design models are developed to design the hybrid generation systems including battery banks and to provide the optimum system configuration. It is ensured that the annualized cost of the systems is minimized while satisfying the required loss of power supply probability. A genetic algorithm is used to find the optimum configuration. Moreover, four membership grades of the systems, such as the reliability, the economical efficiency, the complement and the environmental benefit, are created by a linearly-weighted fuzzy algorithm to search the configuration. The optimal algorithms are applied to a practical project. The optimal design of the project is gotten and analyzed, which shows that the complement and the environmental benefit are taken full advantage of, and the corresponding system cost is minimized with enough power supply reliability.

Introduction

Wind, hydro and solar energy are inexhaustible, renewable and clean energy sources. Some remote areas cannot be supplied by large power grids; however, there are enough resources of wind, hydro and solar energy available. Therefore, hybrid power generation systems can be built by making full use of the complement of these nondepletable energy sources to solve the power shortage problem in the areas.

Present researches of hybrid power generation systems focus mainly on the combination of photovoltaic (PV) arrays and wind turbines [START_REF] Tina | Hybrid solar/wind power system probabilistic modeling for long-term performance assessment[END_REF][START_REF] Markvart | Sizing of hybrid PV-wind energy systems[END_REF], and the best compromise point between system power reliability and system cost is rarely considered in their design. In this paper, the mathematical models of optimal sizing design for the small-sized hybrid power generation system are given. The wind, hydro and solar energy sources are used in the system. Based on the models, a genetic algorithm (GA) and a linearly-weighted fuzzy algorithm are used to get the optimum system configuration that can achieve the required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS).

Models of Hybrid System Components and Battery

A hybrid wind-hydro-solar power generation system consists of PV arrays, wind turbines, hydro turbines, battery banks, an inverter, a controller, cables and other accessory devices. In order to predict the hybrid system performance, each component needs to be modeled and then the generation system can be evaluated to meet the load demand.

The models of three generation components of the system including wind turbines [START_REF] Rosen | The average output power of a wind turbine in a turbulent wind[END_REF], hydro turbines and PV arrays [START_REF] Datta | Photovoltaic output power fluctuations smoothing methods for single and multiple PV generators[END_REF] are proposed in Ref. [START_REF] Zuo | Multi-objective optimal design of hybrid wind-hydro-solar power generation system (in Chinese)[END_REF]. Then, the output power at a given time can be calculated according to the weather data [START_REF] Zuo | Multi-objective optimal design of hybrid wind-hydro-solar power generation system (in Chinese)[END_REF][START_REF] Yang | Weather data and probability analysis of hybrid photovoltaic-wind power generation systems in Hong Kong[END_REF].

The battery [START_REF] Guasch | Dynamic battery model for photovoltaic applications[END_REF] can only be charged to rated capacity, and be limited by the maximum permissible depth of battery discharge when discharging. Therefore, modeling the charging-state of battery is necessary. At any time the state of battery is related to the previous state of charge and to the energy production and consumption situation of the system during the time from t-1 to t [START_REF] Diaf | A methodology for optimal sizing of autonomous hybrid PV/wind system[END_REF]. During the charging process, when the total output of the generation units, i.e. the PV modules, the hydro generators and the wind generators, are greater than the load demand, the battery bank capacity available at time t can be described by

bat inv L g bat bat t E t E t E t E η η σ ⋅ - + - ⋅ - = ) / ) ( ) ( ( ) 1 ( ) 1 ( ) ( . (1)
where E bat (t) and E bat (t-1) are the battery bank capacity available at time t and t-1, respectively, bat η is the battery efficiency,σ is the self-discharge rate of the battery bank, E g (t) is the energy generated by the generation units, E L (t) is the load demand at time t,and inv η is the inverter efficiency.

On the other hand, when the load demand is greater than the energy generated, the battery bank is in discharging state. Therefore, the battery bank capacity available at time t can be expressed as

)] ( / ) ( [ ) 1 ( ) 1 ( ) ( t E t E t E t E g inv L bat bat - - - ⋅ - = η σ . ( 2
)
At any time, the storage capacity is subject to the following constraints:

max min ) ( bat bat bat E t E E ≤ ≤ . ( 3
)
where E batmax and E batmin are the maximum and minimum allowable storage capacity, respectively. E batmax can be taken as the nominal storage capacity C batn , and E batmin can be given by batn bat

C DOD E × = min . ( 4
)
where DOD (%) represents the maximum permissible depth of battery discharge.

Power Reliability Model Based on LPSP Concept

LPSP [START_REF] Diaf | A methodology for optimal sizing of autonomous hybrid PV/wind system[END_REF][START_REF] Ai | Optimum sizing of PV/wind hybrid system (Ⅰ) CAD method (in Chinese)[END_REF] is defined as the probability that an insufficient power supply results when the hybrid system, including the PV arrays, the wind turbines, the hydro turbines and the battery storage, is unable to meet the load demand. Values of LPSP range from 0 to 1. A LPSP of 0 means that the load is always satisfied, and a LPSP of 1 means that the load is never satisfied. LPSP can be expressed as

{ } . ) t ( E ] E ) 1 t ( E ) t ( E [ ) t ( E LPSP T 1 t L T 1 t inv min bat bat g L ∑ ∑ = = - - + - = η (5)
where T is the operation time, normally T is one year.

When LPSP is computed, the input data set consists of daily or hourly solar irradiation on a horizontal surface, the mean values of the ambient temperature, the wind speed and the water current velocity, the load power demand during the year, the technical specifications of the system components, and so on.

Economic Model Based on ACS Concept

ACS [START_REF] Yang | Optimal design and techno-economic analysis of a hybrid solar-wind power generation system[END_REF] is composed of the annualized capital cost C acap and the annualized maintenance cost C amain . The cost of five main components needs to be considered, such as the wind turbines, the hydro turbines, the PV arrays, the battery and the other devices. Then the ACS can be expressed as amain

C C ACS acap + = . (6)
The annualized capital cost of each component takes into account the installation cost, and is calculated by

1 ) 1 ( ) 1 ( - + + ⋅ ⋅ = Yproj Yproj cap acap i i i C C . ( 7
)
where C cap is the initial capital cost of each component, Y proj is the component lifetime, i is the annual real interest rate. The lifetime value Y proj of some devices can be referred to Ref. [START_REF] Chen | Fixed assets directory of State Grid Corporation[END_REF].

The annualized maintenance cost can be estimated by two different ways. First, under normal circumstances, the maintenance cost of wind turbines within service life is 20% to 25% of the total cost, while the hydro turbine is 10%, the PV module is 5%, and the battery is 10%. Second, it can be considered to be a static value which depends on practical statistics.

System Optimization Model with GA

GA is an advanced search and optimization method [START_REF] Bala | Optimal design of a PV-diesel hybrid system for electrification of an isolated island-Sandwip in Bangladesh using genetic algorithm[END_REF][START_REF] Yang | Optimal sizing method for standalone hybrid solar-wind system with LPSP technology by using genetic algorithm[END_REF] and is generally robust in finding global optimal solutions, particularly in multimodal and multi-objective optimization problems.

An optimization model is developed here to obtain the optimum size or the optimal configuration of a hybrid wind-hydro-solar system including a battery bank in terms of the LPSP technique and the ACS concept using GA. The flow chart of the optimization process is illustrated in Fig. 1. The decision variables in the optimization process are the wind turbine power P WT , the hydro turbine power P HT , the PV module power P PV and the battery capacity C bat . Daily or hourly weather data of typical year includes the solar radiation on the horizontal surface, wind speed and water current velocity, and so on.

Combining the ACS with the LPSP based on a penalty function, the fitness function F fitness can be expressed as

2 %)] 5 . 0 , 0 [max( - ⋅ + = LPSP M ACS F fitness . ( 8
)
where M is penalty factor. The power outputs of the generation units are calculated by using their specifications as well as the weather data. The system configuration is then optimized by employing the genetic algorithm, which dynamically searches for the optimal configuration to minimize the fitness function. For each system configuration, LPSP of the system is examined to judge whether the requirement, i.e. LPSP target, can be satisfied. Then, the configuration of smaller fitness is subjected to the following crossover and mutation operations of GA in order to produce the next generation population until the criterion is satisfied. The optimal configuration can finally be found by achieving the lowest ACS while satisfying the LPSP requirement.

System Optimization Model with Linearly-weighted Fuzzy Algorithm

The system optimization model based on linearly-weighted fuzzy algorithm [START_REF] Zuo | Multi-objective optimal design of hybrid wind-hydro-solar power generation system (in Chinese)[END_REF][START_REF] Jeong | Energy management strategies of a fuel cell/battery hybrid system using fuzzy logics[END_REF] considers four optimization objectives: the reliability, the economical efficiency, the complement and the environmental benefit. Each configuration has four membership grades according to the four objectives:

) , , , (

3 2 1 0 r r r r r = . ( 9
)
Different users have different requirements to the four objectives, so a set of weight is created:

} , , , { 3 2 1 0 a a a a A = . ( 10
)
Thus, the objective function is expressed as

3 3 2 2 1 1 0 0 min a r a r a r a r f ⋅ + ⋅ + ⋅ + ⋅ = . ( 11
)
When the objective function is minimized, the optimal configuration is found. The membership grade of reliability is the same as the LPSP, and the membership grade of environmental benefit is proposed in Ref. [START_REF] Zuo | Multi-objective optimal design of hybrid wind-hydro-solar power generation system (in Chinese)[END_REF].

Membership Grade Model of Economy

Based on the ACS model, the cost per kWh of the hybrid generation system can be expressed as

year kWh E ACS C = . ( 12
)
where C kWh is the power generation system cost per kWh, E year is the annual energy production of the hybrid generation system. The membership grade function of economy is:

acp kWh 1 C C 1 - = µ . ( 13
)
where C acp is maximum cost per kWh acceptable to users.

Membership Grade Model of Complementary

The complementary of the hybrid system is measured by the difference in magnitude between the power consumption needed by the load and the total energy production generated by the generation units. The membership grade function of the complement can be expressed as

yearL year yearL year 3 E E ) E , E min( + = µ . ( 14
)
where E yearL is the total load power consumption for one year.

Result and Discussion

The proposed methods are applied to design one demonstration project, which is built to supply power for a village committee in a remote village of a county, north China. The village is centered at 40°23′ north latitude and 116°52′ east longitude, 71.8 m above sea level. At the measured site, the effective wind power density is 43.26 W/m 2 , the average annual wind speed is 2.37 m/s, the average annual solar radiation is 5545.6 MJ/m 2 . The useful waterfall height is 65 m, the average water current velocity is 0.036 m 3 /s in the summer and autumn.

Based on the models of the hybrid wind-hydro-solar generation systems developed in this paper, the optimal configuration of the system fulfilling the requirements is found out. The calculation results are shown in Table 1, where method 1 is the algorithm based on GA, and method 2 is the algorithm based on the linearly-weighted fuzzy algorithm.

Optimal sizing results of method 2 are the specifications of the products available from manufacturers. As shown in Table 1, similar optimal configurations of the two methods are gotten. But the annualized cost of system of method 2 is smaller because environment benefit is in consideration. The design of the hybrid generation system is not simply a combination of wind, hydro and solar generation units. In order to make the most efficient use of energy resources and save investment, optimal methods in consideration of complementary characteristic among wind energy, solar energy and hydro energy should be used to find a scientific and reasonable configuration of each unit. At the same time, a battery bank is absolutely necessary to ensure system power reliability. Without it, the load demand can hardly be met continually.

The LPSP concept used in this study is a statistical parameter decided by users. It is clear that higher power reliable systems are more expensive than lower requirement systems. A smaller LPSP, i.e. higher reliability of the systems, results in high cost of the system and vice versa. Choosing an optimal system configuration according to the system power reliability requirement can save investment and avoid blind capital spending.

Moreover, the loss of load probability [START_REF] Deshmukh | Modeling of hybrid renewable energy systems[END_REF] may be used as the reliability criteria for the hybrid renewable energy system, too. Similar results can be gotten.

Conclusions

The optimum design sizing methods for the hybrid wind-hydro-solar systems based on the genetic algorithm and the linearly-weighted fuzzy algorithm are developed. The two methods can be used to search the system optimum configuration which can achieve the desired LPSP with minimum annualized cost of system. Taking requirements of the users and environmental benefit into consideration, the model based on linearly-weighted fuzzy algorithm is more adaptive to different designing condition.

The proposed methods are applied to design a demonstration project of the hybrid wind-hydro-solar system. With the daily measured field data of the studied project in one year, the energy contribution of the generation units, the battery working state and the energy balance of the system are investigated. The good complementary characteristic among wind energy, solar energy and hydro energy is found out.

Fig. 1 .

 1 Fig. 1. Flow chart of the optimal sizing model using GA

Table 1 .

 1 Optimal sizing results

		Results of method 1 Results of method 2
	Wind Turbine Power (W)	2036.1	1000×2
	Hydro Turbine Power (W)	2925	3000
	PV Array Power (W)	731.5	32×23
	Battery Bank Capacity (Ah)	469.2	120×4
	ACS (RMB Yuan)	8627.99	7265
	LPSP	0.4991%	0.4810%

* Supported by National Key Technology R&D Program for the 11th Five-Year Plan (2006BAJ04B03).


Fan Zhang

Guifa Teng

Jieyao

Research Research on on on on Influenced Influenced Influenced Influenced Factors Factors Factors Factors about about about about Routing Routing Routing Routing Selection Selection Selection Selection Scheme Scheme Scheme Scheme in in in in Agricultural Agricultural Agricultural Agricultural Machinery Machinery Machinery Machinery Allocation Allocation Allocation Allocation

Keywords: Agricultural machinery allocation, hierarchy analysis method, fuzzy consistent matrix

Routing allocation scheme is a core issue in agricultural machinery allocation. It is related to the allocation cost, income and timeliness of crop harvest. How to select a farmland spot to harvest, which needs to consider not only the distance between a farmland spot and an agricultural machinery spot, but also road condition, farmland area, types of agricultural machinery and weather conditions. It analyzes the main influenced factors of routing selection solution in agricultural machinery allocation and has built an improved hierarchy analysis model about these influenced factors in this paper. For the uncertain influenced factors or influenced factors with incomplete information, it adopts an improved fuzzy hierarchy analysis method to evaluate the factor weights in the paper. And the resolving power is improved and the believable degree of the routing selection solution is increased. The evaluation model has supplied a good scientific basis for further research.

Introduction Introduction Introduction Introduction

During harvest time, agricultural machinery users choose proper routing and harvest crops for peasants timely and try to reduce allocation cost and obtain more income, according to crop areas, road conditions, distances of path and so on. How to select a farmland spot to harvest, which needs to consider not only the distance between a farmland spot and an agricultural machinery spot, but also road conditions, farmland areas, types of agricultural machinery and some sudden accidents. The optimal routing solution with lower cost and more economic benefits can be chosen under such factors.

The reasonable allocation solution is the one with shortest distance, the minimum cost and the least to complete the harvest task. It means the allocation routing from agricultural machinery spot to the object farmland spot is affected by more than one target. These targets may be the maximum profits, the minimum allocation risks, the shortest allocation time and so on. Generally, the choice of multi-objective allocation solution may have conflicts between different targets. For example, allocation time is shorter, while the cost will not be little; or the cost is smaller, but the time will not be shortest one.

Description Description Description Description of of of of A A A Allocation llocation llocation llocation P P P Problem roblem roblem roblem

China is a large agricultural country which has vast territory, complex geographic environment and different natural conditions. Agricultural production has obvious variances about time and region. Some peasants have reaped crops spontaneously in some areas using combine harvester and already have achieved great economic results. However, there are still many problems in agricultural machinery allocation during busy seasons. The peasants don't have proper allocation solution, and then they have to waste much time in changing areas, which has bad effects on oil, human, agricultural machinery, and even farm work may be delayed. On the other hand, road conditions, weather and farmland areas should be considered before agricultural machinery allocation. The routing selection for agricultural machinery allocation is based on these influenced factors in this study.

This paper mainly analyzes six influenced factors, just as follows: (1)Allocation Time: Crop harvest has timeliness which should be fulfilled in a certain time range. Earlier or later than the time range will increase the allocation costs. So the time is one important factor for agricultural machinery allocation.

(2)Farmland Area: The more farmland areas, the more incomes for agricultural machinery owners.

(3)Allocation Distance: Different allocation schemes have different distances, and the cost for allocation is determined by the allocation distance directly. The less distance, the less allocation cost. (4)Road Condition: Roads can be divided into several types according to road grades. The different road grade has different influence on allocation time.

(5) Price: Different regions have different harvest price in unit areas, which will directly affect the incomes. (6)Weather Condition: Weather can impact the allocation time, and then affect the allocation income.

I I I Improved mproved mproved mproved Analytics Analytics Analytics Analytics Hierarchy Hierarchy Hierarchy Hierarchy Process Process Process Process in in in in the the the the A A A Agricultural gricultural gricultural gricultural M M M Machinery achinery achinery achinery A A A Allocation llocation llocation llocation

As the complexity of objective things and ambiguity of human thoughts, decision-makers are difficult to determine the attribute weights. The incomplete weights information will lead to the uncertainty of solution selection. One method is to analyze the inherent characteristics of objective data by mathematical programming. And the other one depends on subjective judgment of decision-makers completely. While it may cause errors or inconsistent judgment, and the evaluation result may be easily influenced by the understanding level of decision-makers.

Traditional Traditional Traditional Traditional Analytics Analytics Analytics Analytics Hierarchy Hierarchy Hierarchy Hierarchy Process Process Process Process

AHP (Analytics Hierarchy Process) is one multi-target decision analysis methodology combining with qualitative and quantitative analysis, proposed by T.L.Satty, an American operational research experts in 1970s. AHP can solve decision-making problems with complex structures, many constrained criterions and uneasily be quantized. It can combine with qualitative and quantitative analysis to convert complicated problems into several levels and several parts, and establish a hierarchy structure model. The difficult decision-making problems transform into factors in the same level comparison with each other and build judgment matrix. The weights values of different influenced factors can be obtained through transforming and calculating fuzzy consistent matrix, and supply a scientific basis for the optimal routing solution.

Improved Improved Improved Improved Analytics Analytics Analytics Analytics Hierarchy Hierarchy Hierarchy Hierarchy Process Process Process Process

Traditional AHP is greatly impacted by human subjective judgment and preferences while making decision. This paper introduces fuzzy consistent matrix in the AHP, and overcomes significant differences between consistency of judgment matrix and consistency of human thinking.

The sorting method adopted in this paper can increase the resolution of factors important degree, and also can analyze the sensitivity of sorting results with choosing different values for parameter α , which will help decision-makers to make optimal decisions [START_REF] Zibo | Research on Evaluating Urban Expressway Construction Risk Based on Fuzzy Analytic Hierarchy Process[END_REF]. The flow chart of influenced factors analysis about routing selection in the agricultural machinery allocation is shown in figure1.

The The The The C C C Construction onstruction onstruction onstruction of of of of J J J Judgment udgment udgment udgment M M M Matrix atrix atrix atrix in in in in H H H Hierarchy ierarchy ierarchy ierarchy A A A Analysis nalysis nalysis nalysis

Traditional AHP quantifies human judgment, builds judgment matrix and calculates weights values by nine-scale representation method. To a great extent, this method depends on human experiences, and it is difficult to eliminate decision-maker's one-sidedness. In terms of psychological, the classification in the nine-scale representation method is beyond human's judgment. It increases the difficulties of judgment and can supply unreal data easily. According to the existed shortcomings, this paper defines the weights in evaluation system by three-scale hierarchy analysis method. Table Table 1 1 1

Table Table

= ⎪ ⎪ ⎭ ⎪ ⎪ ⎬ ⎫ ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ = n n 2 n 1 n n 1 2 2 1 2 n 1 2 1 1 1 A A ... A A A A ... A A ... A A A A A A ... A A A A D ⎪ ⎪ ⎭ ⎪ ⎪ ⎬ ⎫ ⎪ ⎪ ⎩ ⎪ ⎪ ⎨ ⎧ nn n2 n1
⎭ ⎬ ⎫ ⎩ ⎨ ⎧ = 1 0 2 1 D
the judgment matrix in the second level is as follow:

⎭ ⎬ ⎫ ⎩ ⎨ ⎧ = 1 2 0 1 11 D , ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ = 1 2 0 1 12

D

The judgment matrix in the third level is as follow:

⎪ ⎭ ⎪ ⎬ ⎫ ⎪ ⎩ ⎪ ⎨ ⎧ = 1 2 0 0 1 0 2 2 1 24 D 4.

4.4.4.Fuzzy Fuzzy Fuzzy Fuzzy C C C Consistent onsistent onsistent onsistent M M M Matrix atrix atrix atrix

The traditional AHP method should inspect consistency test of judgment matrix. When it is inconsistent, the judgment matrix should be adjusted to it. So the judgment matrix may be consistent through several times of adjustment and examination. Although some scholars have proposed some improved method, in fact, they are also difficult to realize.

There are two ways to research whether the consistency between judgment matrix and human critical thinking have coordination or not. One is the direct method, and the other one is indirect. In order not to make the problems return to be complicated, which already have simplified, and try to make the decision-making process be closed to actual human thinking process, this paper adopts the indirect method. From the viewpoint of information theory, the consistent problem can be solved by fully using the known judgment information through clever mathematical transformation. In fact, it is impossible to assure that the judgment matrix has complete consistency, especially for multi-target problems. However, the requirement should be broadly consistent.

The consistent test criterion of judgment matrix is just empirical data and lack of scientific and effective certification. So the fuzzy consistent matrix is introduced to solve consistent test problem in this paper.

n n× = ) r ( R ij , meets: 1 0 ≤ ≤ ij r ,(i,j=1,2,…,n), so R is called fuzzy matrix[1]. Definition2 If fuzzy matrix n n× = ) r ( R ij meets, k j, i, ∀ , 5 . 0 + - = jk r r r ik ij
,so fuzzy matrix R is called fuzzy consistent matrix [START_REF] Lv Yuejin | A New Way for Improving Consistency of the Fuzzy Complementary Judgment Matrices[END_REF].

Conversion Conversion Conversion Conversion of of of of Fuzzy Fuzzy Fuzzy Fuzzy Judgment Judgment Judgment Judgment Matrix Matrix Matrix

After normalized the judgment matrix

+ -=

. Using it, the matrix R can be converted into fuzzy consistent matrix D.

Compared to the traditional Analytical Hierarchy Process, Fuzzy Analytical Hierarchy Process has the following advantages.

(1) Compared with testing whether the judgment matrix has the consistency through calculating its largest eigenvalue and its corresponding eigenvector, it is easier for fuzzy AHP to test whether the fuzzy matrix has the consistency.

(2) The fuzzy inconsistent matrix elements can be adjusted to be consistent quickly, and the weak point,which is the complicate judgment process with several adjustment and inspection, can be overcome by Fuzzy Analytical Hierarchy Process method.

(3) The matrix consistency criterion of fuzzy AHP is more scientific, accurate and simple than the traditional one. Fuzzy consistent matrix meets consistent condition, so matrix D never need to consistent test. Judgment matrix R is converted into fuzzy consistent matrix D: The factors weight value of the level in the fuzzy consistent matrix is calculated by the following formula in the paper [START_REF] Lv Yuejin | A New Way for Improving Consistency of the Fuzzy Complementary Judgment Matrices[END_REF]. 3, Table 4, Table 5 and Hierarchy structure model in section 4, the final optimization routing scheme can be expressed in Table 6. road condition Seen from the above tables, the influenced weights of road condition, allocation distance, machinery types, farmland areas and sudden accidents, which are achieved by methods in the paper, supply a good theoretical support.

T T T

T T T

Conclusions Conclusions Conclusions Conclusions

This paper mainly discusses the influenced factors in agricultural machinery allocation, and analyzes the effects that the factors by improved AHP method under the circumstances of uncertain influenced factors or influenced factors with incomplete information. The formula for calculating and sorting the weight values of factors, which is adopted in this paper, can supply higher resolution when amount of factors is big and has better flexibility and practicality. This paper proposes a good decision-making idea for routing solution about agricultural machinery allocation. And it also supplies a better scientific basis for further research.

Fig1. 4 4 4 4 1

 441 Fig1. Flowchart of Influenced factors analysis in the agricultural machinery allocation solution

Fig2. Hierarchy structure model in agricultural machinery allocation 4

 4 Fig2. Hierarchy structure model in agricultural machinery allocation

  According to the meaning of three scale representation, ij d =0, 1, 2 comparison, the judgment matrix in the first level can be obtained as follow:

1

 1 Definition Definition Definition Definition of of of of Fuzzy Fuzzy Fuzzy Fuzzy Consistent Consistent Consistent Consistent Matrix Matrix Matrix Matrix Definition1 If Matrix

1 .

 1 (i=1,2,…,n). The conversion formula is as follow:

4

 4 Weights Weights Weights Weights of of of of I I I Influenced nfluenced nfluenced nfluenced F F F Factors actors actors actors and and and and S S S Sorting orting orting orting

Table able able able 4 4 4 4

  The influenced factors and weight values of Allocation Cost

	Factor	Farmland Areas	Price
	Allocation Benefit	0.625		0.375
	T T T Table able able able 5 5 5 5 The influenced factors and weight values of Routing Optimization Scheme
	Factor		Allocation Benefit	Total Costs
	Routing Optimization Scheme	0.625	0.375
	Combing with

Table 2 ,

 2 Table

Table able able able 6 6 6 6

  The influenced factors and weight values of Routing Optimization

	Factor	Farmland	Price	Allocation	Weather	Road
		Areas		Distance	Condition	Condition
	Routing	0.391	0.234	0.293	0.035	0.047
	Optimization
	So Routing Optimization=

*Corresponding author: Graduate School, Agricultural University of Hebei, PRC. E-mail: tguifa@hebau.edu.cn

α is a kind of measure about objects difference degree that perceived by people.

This formula has flexibility. The bigger the value of α , the smaller difference of weighs; meanwhile the smaller the value of α , the bigger difference of weighs.

, the weight difference can achieve to the biggest value. Therefore, the smaller value of α indicates that decision-makers may pay more attentions on the difference of important degree between elements [START_REF] Jibin | Research on Priorities of Analytic Hierachy Process and Problems of Fuzzy Multiple Attribute Decision Making[END_REF]. After experiment this paper, the value of α in the first level is chosen as 3, the value of α in the second level is chosen as 4. The weight values can be calculated as follows.

Foundation Foundation Foundation Foundation of of of of the the the the C C C Comprehensive omprehensive omprehensive omprehensive E E E Evaluation valuation valuation valuation M M M Model odel odel odel

The comprehensive evaluation model is based on the hierarchy structure model of agricultural machinery allocation. After comparing the importance of factors and calculating the weight value of factors in the level, sorting is from the bottom up. Then weights value in the evaluation model of the optimal routing solution will be obtained.

In accordance with agricultural machinery allocation, the evaluation model is founded as follow.

ω . i C stands for i th influenced factor, i ω means the weight value of i th factor to the object in previous level.

After discussion, the model is shown in the following tables.

T T T


Hu Zhao

Sangen Wang

Dake Wu

Pervasive Agricultural Environment Monitoring System Based on Embedded Database

Keywords: The pervasive computing, SQLite, WSN, ARM, GSM

According to space constraints of the traditional agricultural environment monitoring system, a small database called SQLite was transplanted into the ARM and Linux operation which could store and manage the field information. Users can get information anytime and anywhere through the GSM (Global System for Mobile Communications)network and the WSN(Wireless Sensor Network) by this design, instead of inquiring data from the PC(Personal Computer) which was widely used now. Sensors embedded in the environment, information spread in the air and captured by the handheld instruments, which is a typical application of pervasive computing.

Introduction

Wireless sensor network of the field monitoring was constructed by Han [START_REF] Huafeng | Design and application of ZigBee based telemonitoring system for greenhouse environment data acquisition[END_REF] to transmitting the field acquired data to the designated database server on PC based on the ZigBee protocol, and data warehousing and decision support was used at the national agriculture statistics service by [START_REF] Yost | Data warehousing and decision support at the national agriculture statistics service[END_REF]. The most recently study was the WSN(Wireless Sensor Network) system based on the infield soil water content monitoring designed by Jackson T, Mansfield K, Saafi M and so on (2007). Data of field were transmitted to the monitoring center through the GPRS network or Internet, and processed by the database system on PC in these systems. The field or greenhouse could be monitored remotely by the operator only in the monitoring center. These systems were expensive and complicate. So they were used only by universities and research institutes, but not in the vast rural areas.

The era of pervasive computing is coming with the development of technology. People can get information anytime and anywhere by using a handheld device. It has already been used widely in the Mobile TV, 3G phones and so on. In this article, a pervasive agricultural environment monitoring system based on embedded database was proposed to achieve the following objectives:

(1) The greenhouse environmental data are transmitted to control module through WSN;

(2) The embedded system can collect, store and process the data independently;

(3) The system can send SMS to remind the users automatically at appointed time or the moment of abnormal;

(4) Users can query real-time and history data anytime and anywhere through mobile phones.

Hardware Design

The system is composed of control module and monitor units. The control module include MCU (Microprogrammed Control Unit), store module, GSM module and RF module; The real-time environmental information is detected and transmitted to the control module by monitor units scattered around the greenhouse. Structural diagram of the system is shown in Figure 1.

The S3C2410 is used to manage data and control other modules in the control module. The NOR FLASH of 16MB is the program memory of the system with the read-write file system. Environmental data will be protected in the FLASH when the power is off suddenly. There are 6 Banks in the S3C2410, and 2 of them were assigned to the ROM, SRAM and SDRAM respectively. The SDRAM of the system is 2 HY57V561620CT of 32M bytes. The TC35 of Siemens works as SMS Module, and the RFM12 transmits and receives data as the wireless modules.

The monitors units include the MCU, sensors and the RF module. They spread around the greenhouse detecting the environmental information. The core of monitor units is a PIC16F877A with the strong anti-interference ability and rich resources.

Control Module

The ARM processor has becoming the preferred choice for the embedded system design now. The S3C2410 is selected as the MUC considering the factors of the processing speed, storage space, interface and the power consumption. The core of the S3C2410 is an ARM920T and it can save cost and improve the performance with advantages of low prices, high performances and high level of integration. The DS1302 provide accurate date and time.

WSN Module

The star-type network topology was adopted for the WSN module. The Unique ID was corresponded to the different monitoring units. A number of monitoring units spread around the greenhouse which sent one packet per second. The control module received data by identifying the ID code circularly. For example, the control module received and processed the data of the second units only after receiving the first by identifying the ID code, and so on. The architecture of the WSN system is shown in Figure 2.

The RFM12 is a low costing ISM band transceiver RF module implemented with unique PLL. Its work signal bands ranges from 315/433/868/915MHZ, complying with FCC, ETSI regulations. The SPI interface is used to communicate with the MCU for the parameters setting. The data are sent into internal registers of RFM12 at the rising edge of the clock when the chip selecting signal nSEL is low. The synchronization header is sent at first, and then the data needed are sent. The next data can be sent when the SDO pin is high. The nIRQ pin is pulled low to notice the MCU when the RFM12 receives data. The MCU then reads the data stored in the buffer temporarily. The receiving register is closed when the receiving procedure finish.

The greenhouse's information includes the air temperature and humidity, soil temperature and humidity, light intensity and CO 2 concentration. Digital sensors have the priority because of the calibration-free and high precision. It also makes it easy for multisensor fusion. The main performances of sensors are described as following:

(1) Air temperature and humidity sensors-Used to detect the air temperature and humidity, and the model is the SHT10 (Basic type), the main performances: the humidity measuring range 0~100%rh, temperature measuring range -40~+123.8℃, Temperature accuracy ±0.3℃(25℃), humidity accuracy ±1.8%RH, Response time 8s, power supply 2.4~5.5V, and the Digital output(I2C bus).

(2) Soil temperature and humidity sensors-Used to detect the soil temperature and humidity, and the model is the SHT10P(Protection type), the main performances: the humidity measuring range 0~100%rh; temperature measuring range -40~+123.8℃; Temperature accuracy ±0.3℃(25℃); humidity accuracy±1.8%RH; Response time 8s; power supply2.4~5.5V; and the Digital output(I2C bus).

(3) Light intensity sensor-Used to detect the light intensity, and the model is the BH1710FVC. The main performances: the measuring range 0 ~ 65,535Lx; Sensitivity±15%; power supply 2.4~3.6V; and the Digital output(I2C bus).

(4) CO 2 concentration sensor Used to detect the CO 2 concentration, and the model is H-550. The main performance: the measuring range 0~5,000ppm; sensitivity ±30ppm±5%(0~50℃); response time≤30s;power supply DC9V~18V;Current consumption 50mA(mean value);and the Digital output(I2C bus)

GSM Module

The TC35 is an industrial grade GSM module integrated RF modules and the baseband processor. It Supports the data, voice, SMS and fax [START_REF] Liao Dianxin | A Survey of the Pervasive Service[END_REF]. The IGT pin maintains low(at least 100ms) at first when the TC35 starts. Then it keeps high to ensure TC35 working. The input/output interfaces of TC35 use a serial asynchronous receiver, meeting with the ITU-TRS232 interface standard. The data of the interface disposition are 8 bit data position, one stop position, and do not have the verification position. The hardware handshake signal uses the RTSO/CTSO. The software flow is controled by using the Xon/Xoff. The TC35 supports the AT command set.

Software Design

The software architecture of the data analysis unit is shown in Figure 3. The embedded operating system µCLinux is located at the lowest layer. It manages the software and hardware resources of the system. The user programs are located at the most top layer. It receives and analyzes the environment parameters, and makes assess of the greenhouse' state. The database management system based on SQLite is located at core intermediate level. It is composed of the numerous program modules and the function is to organize, manage and deposit the shared data effectively.

Design of embedded database system

The µCLinux is an open project funded and maintained by Lineo. It was made popular by its ability to run on processors without memory management hardware and with a typical kernel footprint of only 512 Kbytes or less.

•It inherits stability and robustness from Linux;

•It supports a large number of devices. file systems, and networking protocols;

•No license fee leads to very low cost;

•It gives developers complete visibility of the source code based on GPL license;

•Kernel can be customized on demand.

The software platform was built on µClinux.

The system establishes cross compiling environment by using arm-elf-tools-20030314.sh. This program is copy to the root directory first and run, then µCLinux can be compiled and transplanted. A directory called "images" will be generated in the µClinux-dist/ directory after cross-compilation successfully. It includes 3 binary file: image. ram, image. rom and romfs.img。

The Bootloader is designed to make it sure that the execution jumps to the location of the kernel when resets. The MCU core must be initialized and extended in order to achieve the main functions. The whole procedure consists of the following steps:

(1) Establishing interrupt vector table;

(2) Initialization of various processor models;

(3) Introduction of special variables;

(4) Initialization of memory;

(5) Copy codes. The right to use the CPU is returned to the operating system after hardware initialization process. Then the ultimate goal of the Bootloader is completed.

Structfile_operations structure is used to define the various operation set of equipment in the µCLinux kernel. The main purpose of writing character device drivers is to achieve the various functions of structfile_operations structure. Structfile operations structure is defined in /include/linux/fs.h file. The drivers include the RF module, store module and GSM module. They were turned on and off by following functions:

(1) RF12_open()/RF12_close();

(2) FLASH_open()/FLASH_close();

(3) TC35_open()/TC35_close().

Design of embedded database system

Large amounts of environmental information are collected and processed in the system. The Database is needed to store and manage a large amount of data.

Traditional enterprise databases such as Oracle, Sybase and so on have the difficult to work in the embedded system because of the limited resources. The SQLite is small, fast, and reliable, so it is a good choice for embedded systems. It has the following advantages compared with the traditional databases:

(1) It can run in the embedded devices whice have less memory and computing power;

(2) It can reset easily when the power or memory capacity runs out;

(3) The cost of establishment and maintenance is very low; (4) It has strong portability to suit the complexity of embedded systems. The SQLite is a universal embedded database. It must be transplanted and compiled before apply to the"S3C2410+uCLinux" environment. The main steps of transplanting are described below:

(1) Download sqlite-2.8.15.tar.gz;

(2) Unzip to directory uClinux-dist/user;

(3) User application settings. The SQLite is added as a user application and converted into a shell. The following files need to be compiled:

(1) uClinux-dist/user/Makefile (2) uClinux-dist/config/Configure.help (3) uClinux-dist/config/config.in Run the program"make menuconfig" of uCLinux after above amendments. Selecte"CustomizeVendor/ User Settings" and "Miscellaneous Applications", There will be a new"sqlite (NEW)" which added by us before. The SQLite will be compiled to parts of romfs at the stage of"make romfs" later. The following documents needed to be added and modified finally, and thus to complete the compilation of the SQlite under uCLinux.

•Modify sqlite/main.mk

•Add sqlite/Makefile •Modify sqlite/src/os.c •Modify sqlite/src/shell.c
The SQLite uses a standardized design structure based on relational database mode. It can be divided into 9 major auto units, as shown in Figure 4. These sub-functional units form the top-down tool chain: compile in the upper layer, query in the middle layer, storage data and operate system interface in the underlying.

The SQLite supports most of the standard SQL92 statement, especially to support the view, trigger and services. The API interface of the C language was provided by the SQLite insteading of the database engine. The access operations of data can be achieved by the way of calling the appropriate API functions directly. The Header file "sqlite3.h" must be referenced by the application program. There are 3 core API functions that should be emphasized:

(

Design of the main procedure

The GSM module runes first when the main program begins. Receiver functions (read_sms()) are called to decoding the short message content when the SMS reaches (InterruptInt_Uart(void) interrupt 4 using 3()). The needed value is read from database and sent to the users(send_sms()). Then environmental information is received from the monitoring units (Receive_data()). The values are saved into database at the setting time. The alarm messages are sent to the users when the environmental values are exceeding the normal value. The flow chart of main procedure is shown in Figure 5.

Design of the RF module

The RFM12 is initialized (Open TX) at first in the sending mode, then calls subfunction "Send_data()" to send environmental parameters. The first two hexadecimal numbers of data are ID(Identify) codes which are unique for each unit.

Test results

The system was tested in the 3rd greenhouse of Southwest University agricultural demonstration center from October 2009 to May 2010. There are 16 monitoring units spreading evenly in the greenhouse. The monitored environmental parameters include the air temperature and humidity, soil temperature and humidity, light intensity and CO 2 concentration. Data are automatically stored every 2 hours.

The SMS will be divided into two parts if the length is longer than 67 characters (including letters, numbers, or punctuation). The SMS query mode is designed as follows:

(1) Send"201005040901",system will return" E24.53℃60.23%S21.54℃72.36% Li31111lx C04237ppm" automatically, mean the parameters of 1st monitoring point are:air temperature 24.53℃, air humidity60.23%,soil temperature21.54, soil humidity 72.36%,light intensity31111lx,CO 2 concentration 4237ppm;

(2) Send"date+te"(air temperature)," date+he"(air humidity)," date+ts"(soil temperature)," date+hs"(soil humidity)," date+li"(light intensity)," date+c02"(CO 2 concentration),system will return the data of the date.

Among the total of 521 text messages, there were 2 errors, 5 missing, and 514 corrects. The data could be store six months until the new covered them automatically. Experiments show that the greenhouse remote monitoring system based on"ARM+ uCLinux+SQLite" performs reliably and efficiently.

Conclusion

The system that combined the pervasive computing with the agricultural information was suitable for the vast rural areas. The most significant features of the pervasive agricultural syetem are low cost, simple to use, and people-oriented design. It can be expected that control center room, wired communication network, and complex database system based on PC would disappear in the future. The farmers can monitor and control the environmental factors of the field or greenhouse anytime and anywhere by handheld devices.

  The next part of the data is the representatives of the air temperature and humidity, soil temperature and humidity, light intensity and CO 2 concentration. The nIRQ pin is pull down at the end of sending to notice MCU sending the next data. The program diagram is shown in Figure6(a).The RFM12 is initialized(Open RX) at first in the receiving mode, The nIRQ pin is pull down when the MCU reads the data. The FIFO must be reset after receiving the packet. The received data can be processed only through the validation procedures. The program diagram is shown in Figure6(b)


Caojun Huang

Lin Li

Souhua Ren

Zhisheng Zhou

Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network

Keywords: GA-BP, soil in the field, moisture forecasting, modeling, forecast precision

Soil moisture forecast model based on genetic neural network is established because the soil moisture forecasting is nonlinear and complex. The weights and threshold value of BP network are optimized according to the total situation optimization ability of genetic algorithm, which can avoid effectively that BP network is vulnerable to run into the local minimum value as its poor total optimization ability. The model is applied to Hongxing farm in Heilongjiang Province to predict the soil moisture. The forecasting result shows that the model has favorable forecasting precision, which indicates that the genetic neural network model is feasible and effective to predict the soil moisture.

Introduction

With the increasing development of industrialization, the human environment and the global extreme climate deteriorates more and more, the drought and flood disaster happen continually. All of these restrict the agricultural production and the healthy development of social economy badly. To ensure food safety, fertility and get in timely, the accurate forecast information of soil moisture of farmland is needed to be achieved in time and exactly. Therefore, establishing the soil moisture prediction model is conducive to science management of farming and has important significance for the food fertility and stable grain.

The different soil moisture prediction models were established by experts and scholars, such as: empirical formula of the model, the water balance model, the dynamic model of soil water, time series model, remote sensing model and neural network model. These models are always restricted in the practical application [START_REF] Hongwei | Research on Soil Moistrue Prediction Models of Inadequate Irrigation Rice Field in South China[END_REF][START_REF] Mahmood | An analysis of simulated long-term soil moisture date for three land uses under contrasting hydroclimatic conditions in the northern greet plains[END_REF][START_REF] Zhou Liangchen | Study on Estimation of Soil-water Content by Using Soil-Water Dynamics Model[END_REF][START_REF] Hexi | Application of Time Series Analysis in Soil Moisture Forecast[END_REF][START_REF]loess soil are[END_REF][START_REF] Xiaogang | Forecast of Soil Moisture Content during Critical Period of Spring Sowing Based on Precipitation in Last Autumn[END_REF]. For example, although the empirical model is simple and useful, the model parameters have limited applied scope. Other models such as the water balance model, the dynamic model of soil water, time series model etc. have better suitability, but they need a mass of measurement data. The remote sensing model has poor stability. Neural network model get into the local minimum values easily, which makes the network cannot search the global optimal solution [START_REF] Chengfeng | Optimized BP neural network classifier based on genetic algorithm for land cover classification using remotely-sensed data[END_REF].

According to the complexity and the shortages of forecast of soil moisture model, a suitable algorithm for processing the non-linear problem and with global optimization ability of Genetic Algorithm (GA) is used. It optimizes neural network coefficients and threshold and form a hybrid GA-BP Algorithm, which has the practical value of the soil moisture prediction model of the soil moisture forecast.

Genetic BP Network Model

The genetic BP network utilizes the overall situation optimizing ability of genetic algorithm to search optimization for weights and threshold value of BP network within large scope, then the optimum solution is endued to the weights and threshold value of BP network, finally BP network is used to search precisely to obtain the optimal weight and threshold value [START_REF] Shiguan | Genetic Algorithm for Optimization of Neural Network Structure and Weight Distribution[END_REF]. The specific implementation process is as follows [9-10]:

Population initialization and coding

Population initialization is used to refers to initialize the BP network's weights and threshold. According to population design experience, the population A with

M individual is produced, { } T m X X X X A , , , , 3 2 1 L
= , individual { } m i x x x x X , , , , 3 2 1 L
= M genes in ( ) m i x x x x X , , , , 3 2 1
L = are composed of BP network's weights and threshold. The length computation is as follows:

2 1 2 1 1 S S S S S r m + + ⋅ + ⋅ = ( 1
)
Where r is the input node numbers, 1 S is the hidden nodes, and 2 S is the output layer nodes.

As the soil moisture prediction is more complicated, the real number coding scheme is chosen, which can effectively avoid weight step change. Next, with real coding method, the vector of three-layers BP networks weights and threshold is:

] [ 2 1 1 2 2 1 1 2 21 1 11 * 2 2 21 * 1 1 11 S S S S S r S S B B B B Xi , , , , , , , , , , , , , , , L L L L L L ω ω ω ω ω ω = (2)
Where 1

ω is the weights value between input and hidden layer, 2 ω is the weights value between output and hidden layer, 1 B is the hidden layer's threshold value, and 2 B is the output layer's threshold value.

Fitness function

The fitness function is used to evaluate individual. Each individual is evaluated according to the fitness function value, then BP neural network input sample is obtained by decoding corresponding individual. Finally, neural network output error is calculated, the fitness function is used:

E 1 F(i) = (3)
Where E is BP network output error, its computation formula is:

2

) ( 2 1 E ∑ - = b Y y ( 4
)
Where y is network actual output, h Y is network standard output.

Choose operation

According to the calculation results of fitness, the highest fitness groups are retained. High fitness individuals are selected to pass down offspring directly. The other individuals are chosen by roulette selection method, so the small fitness of individuals also have the opportunity to pass down offspring. This ensures the diversity of the individuals in the group, and avoids the local optimum. Select formula is as follows:

∑ = = M i is i F i F P 1 ) ( ) ( (5)
Where is P is probability for selection: M is the population size: i is the first i individual in group M .

Crossover operation

Since the weight coefficients are obtained by using real number coding, arithmetic crossover is used. If τ a X and τ b X are two crossover individuals, then the new individuals are generated after operation:

τ τ τ α α a b a X X X ) 1 ( 1 - + = + (6) τ τ τ α α b a b X X X ) 1 ( 1 - + = + (7)
Where α is linear combination coefficient, and usually ) 1 , 0 ( ∈ α .

2.5

Mutation operator

The non-uniform mutation operation can improve the algorithm accuracy, which makes the final search closer to the optimal solution. So, non-uniform mutation is selected. Mutation operating is as follows:

m x is variation point of variance n m x x x x L L 3 2 1 x X = , [ ] b a x m , ∈
, the new genetic m

x ~of variation point can be obtained by:

       - - - - - + = - - ) 1 )( ( ) 1 )( ( x ~) 1 ( ) 1 ( b T t m m b T t m m m r a x x r x b x (8) Where ] 1 , 0 [ ∈ r
,and it has the uniform probability distribution; t is the current evolution algebra, T is the termination algebra, b is shape parameters of the system.

According to [START_REF] Shiguan | Genetic Algorithm for Optimization of Neural Network Structure and Weight Distribution[END_REF], search scope is large in the early stage of algorithm operation(when t is small).While it is small in the late stage(when t is close to T ) ,which is equivalent to local search.

Generate new population

A new population will generates when new individuals are inserted into the primary populations, then the connection weights of individuals in the new population are given neural network, and the new individual fitness function is calculated, if it meets predefined criteria, then goes to the next step, otherwise continues to genetic operation.

Optimal operation

The optimal individuals which have been decoded are endowed to neural network's weights and threshold value as the net's initial value. BP neural network is trained until the error square sum reach the specified accuracy, or reach the maximum iteration times. The flow chart of Genetic neural network algorithm is shown in Figure 1:

Implementation of soil moisture forecast

This research is carried out in Heilongjiang Hongxing farm, which is located in the northern of Heilongjiang province Songnen Plain and Xiaoxing'an Mountain zone, belongs to cool transitional continental monsoon climate. Location: latitude 48°02'~ 48°17', longitude 126 ° 47 '15 ° ~ 127. Annual average temperature is 0.8 ℃, the average frost-free period is117 days, annual rainfall is about 553.0mm. The temperature is below 0 ℃ in five months every year. Hongxing farm has plenty of rain and heat than adequate, long sunshine hours, high soil organic matter content, strong water storage capacity, which provide more advantageous conditions for soybeans, wheat and other crops of high economic value.

Feature extraction of Model input and output

The actual soil moisture data, under10cm, 20cm, 30cm, 40cm and 50cm from 2004 to 2009, is used in the model. The 10 factors effecting soil moisture remarkably, such as temperature, rainfall, evaporation, relative humidity and sunshine hours and other meteorological factors, are applied to the input of the model.

The model output is soil moisture data. According to the training results, it can be known that single output factor has higher precision and faster convergence speed. Therefore, we select single soil moisture data as the model output. Research shows that with the crops growing, the main water area of rhizome has downward trend [START_REF] Xiying | Yuan Xiaoquan: A Field Study on the Relationship of Soil Water Content and Water Uptake by Winter Wheat Root System[END_REF][START_REF] Zhang Yingshan | Croproot system and Soil water application[END_REF][START_REF] Minjian | Principle of cropping system and soil management[END_REF][START_REF] Lijie | Experimental study of Soil moisture content in the Midwest of Jilin province[END_REF]. Therefore, we focus on this layer of the surface 10cm of soil moisture conditions in April and May, in June, layer of the surface 20cm, in July, August, September, layer of the surface 30cm.

Hidden layer and hidden layer nodes determination

As the three-layer neural network can approximate any function, we use a single hidden layer BP network structure [START_REF] Hecht-Nielsen | Counterpropagation Networks[END_REF]. Based on the above analysis, there are ten input factors and one output factor. According to the calculation formula of the hidden nodes [START_REF] Paola | A review and analysis of backpropagation neural networks for classification of remote-sensed multi-spectral imagery[END_REF], the optimal hidden nodes number is 6 to 23. According to the training results, the optimal hidden nodes is 22. Therefore, the network topology is :10-22-1.

Description of training samples

Firstly, the input and output sample data are normalized for network training [START_REF] Ling | Two hybrid learning strategies of feedforward network[END_REF].

Input samples and the corresponding output data are inputted to the network with the recurrence relations, thus completing the genetic neural network model training, modeling process, the input and output data table as follows:

Table1. Correspondence of the input and output data

Input sample data Output sample data n X X X , , , 2 1 L
L n Y Y Y , , , 2 1 L L n n n n X X X + + + , , , 2 1 L
L n n n n Y Y Y + + + , , , 2 1 L
L …… …… n kn kn kn X X X + + + , , , 2 1 L
L n kn kn kn Y Y Y + + + , , , 2 1 L

Forecast implementation

The genetic neural network parameters are set up firstly, then the input and output data are put into the network for training. When the training result is in accordance with established standards, the training stop, the genetic neural network structure and parameters are saved. The trained neural network is used to forecast the soil moisture from April to October in 2009, and the forecast results are compared with BP neural network forecast model results, the detailed as follows: It can be seen from the table that BP network prediction of soil moisture average absolute error is 2.92%; GA-BP network prediction of soil moisture average absolute error is 1.92%. Obviously, GA-BP network prediction accuracy increases one percentage point. The results show that the genetic algorithm of BP neural network has better mapping capabilities and better generalization ability comparing to the single BP neural network. It satisfies modeling requirements and is applicable to the soil moisture forecast.

The soil moisture prediction model was established based on the combination of genetic algorithm and neural network. This model offsets the shortcomings that the neural network for global searching capability is weak and easily fallen into the local minimum. It also gives full play to the neural network nonlinear mapping capability. The model has been applied to Heilongjiang Hong Xing Farm on soil moisture prediction and has achieved good results. Compared to the BP neural network model, the genetic BP neural network model has better prediction accuracy and application value, and favorable application prospect in the soil moisture forecast.

Fig. 1 .

 1 Fig.1. Realizing flow chart of GA-BP

  L

	( 2 X ), April 28 sample data( 3 X ), ... ..., October 18 sample data n X ; output sample
	data arranged in chronological order: April 18 sample data ( 1 Y ), April 28 sample data
	( 2 Y ), May 8 sample data ( 3 Y ), ... ..., October 28 sample data ( n Y ).
	Where 1 X to n X and 1 Y to n Y are input and output sample data in the first
	year arranged by time serial.	X	kn	1 +	to	kn X +	n	and	kn Y	1 +	to	kn Y +	n
	homoplastically. The input sample data includes: 10 ~ 50cm of five levels of soil
	moisture data, temperature, rainfall, evaporation, relative humidity and sunshine
	duration, etc; output sample data is a certain level of soil moisture data
	According to its requirement by Heilongjiang province of soil moisture report, soil
	moisture data should be reported to Land Reclamation Bureau monthly from April to
	October each year on the 8th, the 18th, the 28th. To this end, each of the input sample
	data arranged in chronological order: April 8 sample data ( 1 X ), April 18 sample data

Table 2 .

 2 Comparison of forecasted results by two models

	Date (day/month)	Measured results (%)	GA-BP network model Forecasting Error results(%) (%)	BP network model Forecasting Error results(%) (%)
	18/4	34.98	36.253	1.273	36.676	1.696
	28/4	29.15	29.775	0.625	29.255	0.105
	8/5	17.49	17.363	0.127	15.386	2.104
	18/5	22.79	23.711	0.921	23.627	0.837
	28/5	23.64	24.672	1.032	25.235	1.595
	8/6	33.74	30.381	3.359	29.525	4.215
	18/6	35.52	37.585	2.065	38.61	3.090
	28/6	42.62	37.128	5.492	35.444	7.176
	8/7	40.25	44.842	4.592	43.704	3.454
	18/7	45.51	47.498	1.988	49.681	4.171
	28/7	49.37	50.62	1.25	53.164	3.794
	8/8	43.99	42.98	1.01	39.015	4.975
	18/8	30.54	32.409	1.869	32.227	1.687
	28/8	35.22	32.587	2.633	31.683	3.537
	8/9	38.14	39.48	1.34	40.63	2.490
	18/9	36.62	37.356	0.736	38.034	1.414
	28/9	34.87	32.51	2.36	31.503	3.367


Ermin Zhou

Kelou Chen

Yanrong Zhang

Overall layout design of iron and steel plants based on SLP theory

Keywords: Systematic layout planning, SLP, Iron and steel plant, Overall layout

Abstract:Based on the total production process of iron and steel plant, and with the general design requirements for the major operating units, the major operating sites' overall layout relationship of the iron and steel plant is determined. Based on the production workshops, auxiliary plants, power plants, transportation facilities, the functions of departments, subsidiary production and living services departments' within and outside association, the situation of materials' into and out, systematic optimization layout planning is proposed. With the size and position relationship of the operating units, and other relative limiting condition and common practices, overall layout design of the factory is developed.

Introduction

Many steel companies in China are currently conducting or planning a new expansion of production base, for the internal and external logistics of steel plant are volume, and the status of a serious lack of iron ore, scientific selection of the steel plant site, rational distribution of the various operating units. To find the best spatial location of all facilities for the plant, the enterprise's materials, equipment and human resources are efficiently used, improve the utilization of various resources, to make the most productive, to catch up with the trend of China's iron and steel enterprises development and to fulfill the requirements of a large number of imported iron ore and the special region development. (1) to adapt to urban planning; [START_REF]The preparation group: Steel mills general plan reference[END_REF] the rational use of land; (3) The arrangements for venues; (4) to create collaborative conditions; (5) the effective protection of the environment.

The work sites overall layout of iron and steel plants

The design requirements of the major operating units

(1) Production areas (plant): iron and steel plant production area is the main part of the overall layout, should give priority to select the appropriate location. According to the nature of the steel production plant, A larger percentage of transportation outside the factory, so should close to interface points of the main mode of transportation outside the factory: stations, terminals, roads in the vicinity; production area is large consumption of water and electricity facilities, should close to the water supply and Power supply; Production areas are also produced pollution venue, should be installed in the minimum space required frequency of clean wind upwind side; around the production area may be set aside for the development of room for expansion.

(2) Transport Facilities Location: When using the waterway transport outside the factory, we must select the appropriate to the domestic terminal building sites, particularly the use of raw materials for large ships dock, to be near the production area, and a reasonable convergence conditions. As much as possible make the unloading of materials directly to the production areas, finished packing can be directly transported to the terminal. When use the rail transportation, Should on the basis of convergence conditions, logistics direction, working conditions of the transfer, to select a similar size of the marshalling factories, should leaves the factory to the possible expansion of the scale synchronization. Production areas and cities, terminals, stations, water sources, sewage treatment plants, substations, residue field, construction site, residential sites and adjacent areas such as enterprise collaboration should be roads.

(3) Water and power facilities: the water sources of iron and steel plants are mainly come from the natural water sources such as river or sea . Either surface or groundwater sources, if used as domestic water, should leaves health protection regulations required distance to prevent pollution. Sewage treatment plant should layout in the direction of water discharge, close to the water into point. Treated sewage discharge outlet should be located downstream of water points, and has more than 100m from the health protection.

(4) Health Protection belt: between steel mill plant and other sites or adjacent enterprises, according to the concentration of pollutants emitted by factories, according to local environmental assessment requirements set a certain width of the area of health protection. In the zone, according to functional requirements for green, light pollution, the facilities can be arranged, but not often live in housing arranged.

(5) Java games: slag and industrial waste disposal sites should be selected in the main plant slag and industrial waste discharge direction, make full use of low-lying land, wasteland, valley and beach land. Stockpiling sites may require unified planning, acquisition phases, the stockpiles of the early stage generally not less than 10 years.

Slag and industrial waste should be fully comprehensive utilization, save resources, reduce the impact of keeping occupied land, which is the trend of development.

(6) Residential area: subject to health protection standards and does not affect plant development, should close the factory layout. Between residential areas and the plant should not be arranged the railway and busy highways, residential roads across the region can not be transited, between neighboring cities, passenger stations, terminals and the plant should be roads.

(7) Construction Site: construction site set up for the construction of various facilities. Therefore, its location should choose the fixed end of the factory building, and plant a certain distance between the left does not affect plant development. To facilitate the use of permanent rail, road, water, electricity and other facilities. [START_REF]The preparation group: Steel mills general plan reference[END_REF] Based on the above basic principles and general layout of the design requirements of the major operating units, combined with Baosteel Zhanjiang New Area in the special conditions and the geographical situation of the factory location, environmental factors, to determine the overall layout of the various sites on the iron and steel plant ,shown in figure 1.

Systematic layout planning of iron and steel plant

SLP method Introduction

Systematic Layout Planning was proposed by Richard • Muther of American,and had been widely used. This approach applies not only to the factory layout design and production systems ,also can be used for logistics centers, shops, hospitals, schools, and other service facilities layout design [START_REF] Sun | Systematic Layout Planning of Logistics Center Design[END_REF]. When the SLP method is applied to the overall layout of industrial facilities design, commonly used procedure is to analyze the relationship between production process, and then the logistics of the operation unit and non-flow analysis, and make a weight , to obtain a comprehensive relationship between them, Finally, according to the intensity of the various operating units draw their location diagram.

Production Process

In the iron and steel plant, supply of raw materials and fuel from raw materials prepared workshops to the sinter plant and coking plant workshop, Produced sinter and coke been supplied to the iron workshop,supply the production of hot metal to steel-making plant, supply the liquid steel continuous to casting plant, supply the production of steel ingot to steel rolling plant, Shipped out the production of finished steel products, form of the whole plant production processes. The relationship between the workshop is the production process relations, we must keep this process ,in general can not be changed.

Logistics relations (quantitative relations)

The design according to an annual output of 10 million tons of steel to consider, according to the scale of production, material consumption indicators and quality of materials to calculate the materials number of input and output. According to the quantity of material flow between the production plants, power plants, ancillary facilities, residential areas ,establish the material flow start and end table, letters on behalf of the workshop(A-Raw material, B-Sintering, C -Coking, D-Iron, E -Steel, F-Rolling, G-Thermoelectricity, H-Oxygen, I-Gas, J-Substation, K-Region, L-Residential) ,show in table 1. Relationship between level of non-logistics division, according to the qualitative factors identified above, need to study in detail the relationship between the various workshops, in accordance with the hierarchy of importance of the relationship, show in table 3. According to Table 3, and the importance of their classified nature of relations between requirement levels, rendering non-logistics relationship diagram, shown in figure 3.

Figure4. Integrated relationship diagram

By the more comprehensive relationship, the relationship between the operating units to use as shown in table 5 to determine the connection type, mapping the location of the various operating units of the relevant plan, shown in figure 5.

The general layout plan to determine

The above analysis of various aspects, according to the main production plant and the operating relationship between the location of units, reference auxiliary plant, power plant, residential area, port, railway station interface, and finally determine the overall layout of the design of steel plant design, shown in figure 6.

Summary

Due to the large flow of materials in steel plant, logistics factors in the overall layout design accounts for a large proportion, therefore, this design started centers around the steel mills logistics factor. From the operating unit focused on the logistics relationship and both of non-logistics relationships, obtained comprehensive relations, and by the operating unit of area, green requirements to make the overall layout plan steel plant. Emphasis analyzed from the two aspects of logistics and non-logistics relationship between the various operating units, obtained comprehensive relations, and by the operating unit of area, green requirements to make the overall layout plan of steel plant. The overall layout of the steel plant is difficult to achieve perfection, for the specific internal and external conditions, natural and geographical factors and regional development, etc. Harmful gases and waste water is also not possible have no blunder, more serious pollution should be taken into account, such as focus on solving the sulfur, carbon gases and water.

2. 1 .

 1 The principle of overall layout of iron and steel plants Site in the established arrangement of plants within the various venues, to adapt to urban planning and industrial development zone request, local natural conditions combined rational use of land, fully arranged in various venues, and create synergies.

Figure1.

  Figure1. Steel plant general layout diagram of venues

  set out the logistics amount, material name and relationship level between the workshop, and then according to the logistics amount and the relationship level, drawn logistics relationship diagram, shown in Figure2. From the logistics relationship diagram can be seen the relationship level of each workshop, just the proximity degree, and as a basis for layout.

Figure 2 .relationship diagram 3 . 4 .

 234 Figure 2. Logistics relationship diagram

Figure 3 .

 3 Figure 3. Non-logistics relationship diagram

Figure 5 . 4 . Determine the overall layout of the steel plant 4 . 1 .

 5441 Figure 5. The location-related map of operating units

Figure 6 .

 6 Figure 6.The overall layout plan steel plant。1-raw materials;2-sintering;3-Coking;4-iron;5steel;6-rolling;7-Thermal;8-Oxygen;9-gas; 10-Transmission;11-the total area;12-residential and commercial areas;13-Terminal;14-raw materials Station; 15-the entrance port;16-Trans Station;17-export terminal;18-finished products;19-Sports;20-artificial lake; 21-Development Reserve;22-Railway;23-lawn,flower beds, etc.; 24-bush;25-residue field;26-water station

Table 1 .

 1 Material flow start and end table (ten thousands tons / year)

	END
	A	B	C	D	E	F	G	H I	J K	L
	START
	A	1640 670 370	120		270	310
	B			1560
	C	28		465
	D	140			945
	E					965
	F				87
	G
	H			20
	I					15	15			20
	J
	K
	L

Table 2 .

 2 Material flow classification table

	Serial	Material flow (ten thousands tons	Relationship
	number	/ year)		level
	1	Largest logistics	>1000	A
	2	Large logistics	600~1000	E
	3	Logistics moderate amount of	300~600	I
	4	Logistics small amount of	50~300	O
	5	Logistics less	<50	U
	6	No amount of logistics	0	X

According to material flow start and end table and material flow classification table,

Table 3 .

 3 Non-logistics relationship levels

	Nature of the	Relationsh	Explain
	relationship	ip level
	Absolutely necessary to	A	Consider merger
	close
	specially needs to close	E	Requirement to close
	Need to close	I	Demand to close
	Generally to close	O	A certain distance
	Unimportant	U	Not limited to distance
	Do not need to close	X	No relationship or
			interference
	Steel plant relations of non-logistics factors, just close to ground level, generally
	there are following several, show in table 4.

Table 4 .

 4 Non-logistics relationship factors

	Code	relations of non-logistics factors
	1	The requirements of production management
		relations
	2	Power supply requirements
	3	Security and environmental protection
	4	Maintenance and support facilities requirements

Table 5 .

 5 Relations dense representation

	Level	Coefficient	Line express	Close degree
	A	4		Absolutely necessary
	E	3		Particularly important
	I	2		Important
	O	1		General
	U	0		Unimportant
	X	-1	(No tag)	Unimportant or do not want to

Table 6 .

 6 The main workshop area of land (ten thousands m2)

	Serial number	Shop name	Area
	1	Materials workshop	320
	2	Sintering plant	120
	3	Coking plant	180
	4	Iron plant	150
	5	Steel workshop	150
	6	Rolling shop (plate, thread,	300
		tubes, wheel, etc.)
	7	Thermal power station	80
	8	Oxygen station	20
	9	Gas Station	40


Shi Xiaoxia

Zhang Yaoli

Peng Cheng

Study on Regional Agro-ecological Risk and Pressure Supported by City Expansion Model and SERA Model -A Case Study of Selangor, Malaysia

Keywords: City expansion, Ecological risk, Remote sensing, SERA model

This study revealed the influence of city expansion on the agroecological risks through the analysis and prediction of city expansion in different periods and study on the change of risk and pressure on the regional agricultural eco-environment. The city expansion of Selangor, Malaysia (as a case) was predicted based on relevant spatial and attribute data as well as simulation prediction models of city expansion. Subsequently, the ecological risk and pressure in the study area as well as on regional agricultural land use was assessed through the realization of factors of SERA Model. The results showed that the risk and pressure on agricultural land use was consistent with the level of the urbanization. With the expansion of urban area, the ecological risk on agricultural land use in the study area became greater. The risk and pressure on agricultural land use with city expansion was well analyzed with SERA model.

Introduction

Urbanization aggravation leads to a series of ecological problems, such as land degradation, climate change, etc. The major manifestation of urbanization is the spatial urbanization. With the continuous expansion of urban area, much useable land of other types is occupied. It results in the despoliation of the space and resources of eco-environment, the destruction of the regional eco-environment and brings risks to the regional agro-ecological. The in-depth study on relations between the city expansion and the regional eco-environment, along with the further study on the risks and pressure on agricultural eco-environment would lay the theoretical foundation to improve the agricultural eco-environment and to realize the sustainable development of agriculture. Agricultural eco-environment is a part of the regional eco-environment. However, the relationship between the agricultural eco-environment and the city expansion has not yet been put forward directly. Based on the neural network, some scholars made quantitative analysis on the cultivated land security with the support of the spatial information technology and the study didn't include the time characteristics (Qian [START_REF] Yurong | Quantitative Analysis of Cultivated Land Safety in Zhangjiagang City based on BP-MC Networks[END_REF]. The study on the relationship between environment and city expansion was a qualitative description originally. For instance, Shi Dengrong et al. made qualitative analysis on the ecological problems like atmospheric environment, soil quality, land resources, etc. as well as the social issues caused by urbanization [START_REF] Shi Dengrong | Comparative Study on Air Quality of Several Cities in the Yangtze River Delta[END_REF]. However, this qualitative study neither meets the needs of study on city expansion, nor provides assessment methods and tools in the urban planning of the regional environmental sustainability for the planners and policy makers. The development of the remote sensing technology provides more effective ways and means to obtain the data of city expansion. Thus, the quantitative study on relationship between city expansion and eco-environment was paid attention to by more and more researchers on the urban remote sensing and the ecoenvironment. The study on relationship between city expansion and eco-environment was divided into three aspects: study on relationship between city expansion and ecoenvironment based on the spatial heat environment, which mainly studied the influence of the urban heat island on eco-environment (Lo CP, 1997;[START_REF] Yunhao | Study on Spatial Pattern of Urban Heat Environment in Shanghai City [J][END_REF]; study on relationship between city expansion and eco-environment with the introduction of the eco-environmental index based on the remote sensing data [START_REF] Wilson | Evaluating Environmental Influence of Zoning in Urban Ecosystems with Remote Sensing[END_REF][START_REF] Yue Wenze | An Analysis on Eco-Environmental Effect of Urban Land Use based on Remote Sensing Images: A Case Study of Urban Thermal Environment and NDVI [J][END_REF]; study on relationship between the spatial form of city expansion and eco-environment, which was mainly from the perspective of spatial patterns with the support of spatial information technology.

Study on relationship between the spatial form of city expansion and ecoenvironment was based on the theory of land use change to study the influence of the city expansion spatial form on eco-environment through the variation characteristics of urban spatial form. It gave a certain division standard of evaluating grade (Ma [START_REF] Ronghua | Assessment of the Eco-Environment in Hainan Island based on RS & GIS[END_REF]. Some scholars adopted Ecological Risk Index (ERI) Model and determined the intensity parameters of the ecological risk to complete the digital simulation of the ecological space distribution (Wei Shichuan, 2008). However, the experts' judgment about the intensity parameters of ecological risk in the study increased human disturbance degree in risk value, and the performance of the risk value in space can not be refined. And most of the study on city expansion and regional eco-environment based on GIS and RS was study on spatial change on the ground of the administrative boundaries; some scholars began the regional ecological risk assessment according to the landscape units, but the interaction among ecological units as well as the pressure mechanism on the eco-environment imposed by city expansion was not taken into account in this pattern of environment assessment (MA Shu-ting, 2004). Li Jinggang et al. built the ecological risk assessment model for natural/semi-natural landscape space under the urbanization based on the meso-scale according to the principle of "pressure, exposure and stability". An ecological risk assessment of the urbanization process was conducted in Beijing. Its model was described as follows (Li Jinggang, 2008):

N I F A V     . ( 1
)
In the formula, V A refers to ecological risk of landscape space; , , F I , and N refers to 4 factors of the external accessibility of the landscape, the external pressures, the internal stability and the neighborhood stability respectively.

From the current remote sensing study on relationship between city and ecoenvironment, it can be seen that taking remote sensing as the support, the ecological assessments from the perspective of the regional spatial pattern do not reflect the relationship between the spatial urbanization and the eco-environment clearly; the ecological risk assessments with single scale do not meet the needs of study any more; as to the eco-environment assessments based on the means of remote sensing, most of them have adopted the single factor assessment model, because the multi-factor assessment model of the eco-environment is too simple; as to the remote sensing analysis of eco-environment with the introduction of the eco-environment assessment model under city expansion, the assessment model is too complex and involves too many socio-economic indicators difficult to obtain. The assessment factors like the biomass dynamic changes and the net primary productivity, etc. obtained by the remote sensing methods are not applied effectively; meanwhile, the ecoenvironmental risk assessment under city expansion based on the remote sensing is not proposed directly.

Taking Selangor, Malaysia as the case, through the analysis of relationship between city expansion and eco-environment, with the support of RS technology, the study was based on the remote sensing assessment model of ecological risk, namely, SERA model, under city expansion and analyzed the influence on the agricultural eco-environment imposed by city expansion to determine the risk and pressure on the agricultural eco-environment imposed by city expansion. It would provide technical support to improve agricultural eco-environment.

Methods

The ecological risk and pressure of the study area based on SERA model was simulated in this study. Then, the ecological risk and pressure on agricultural land use in the period of study was analyzed through the ecological risk and pressure imposed of the entire study area.

Overview and Data of the Study Area

The study area includes Selangor, Malaysia and the capital Kuala Lumpur and the administrative capital Putrajaya, with a total area of approximately 8,214km . The 2 study area is located at 101°41′E, 3°09′N on the southwestern coast of Malaya near the equator; the northwest, north, and northeast areas of the study area are occupied by hills and mountains; Klang River and its tributaries Gombak River converge in the city and flow into from the southwest of the Malacca Strait. The climate there is tropical rainforest climate with high temperature and rainy throughout the year; its average temperature is 25℃-30℃; the rainfall is abundant, and the annual average rainfall is 2000mm-2500mm; the rainy months are from March to April and from September to November. There is ample sunshine with high temperature during the day and relatively cool in the afternoon and evening in the study area. This area is often attacked by a sudden storm. The main cash crops in the area are oil palm, rubber trees etc.

The data adopted in this study included spatial data, socio-economic data and field survey data. As the area of Selangor, Malaysia and the capital Kuala Lumpur is large, and 2-scene data covering the entire state was required, the spatial data included 12scene Landsat TM/ETM images of numbering 127/57 and 127/58 in six periods of 1990, 1991, 1994, 1996 , 1998 and 2002, vectorgraph of the entire administrative division, river network, road network, digital elevation model (DEM), the land use type map in 1996; the socio-economic data originated mainly from the "Malaysia Plan" made by the Malaysia Statistical Bureau, including basic socio-economic indicators; the field survey data included the coordinate points of unchanged regions and the economic center regions required for radiometric normalization of remote sensing image supported by DGPS. This study classified the land use based on segmentation results of the Mean-shift algorithm.

Prediction of City Expansion in the Study Area

The urbanization intensity index ( UII ) is one of the important measurement indicators reflecting the expansion characteristics of urban land use, the formula is described as (2):

i t i n t i n t t i TLA n ULA ULA UII / 100 ] / ) [( , , ,      . (2)
Where,

n t t i UII  , t i ULA ,
, and The data of Table 1 was obtained from the classification results of land use type. It can be seen from Table 1 that city expansion of Selangor from 1990 to 2002 was extremely rapid. From 1994 to 1998, the city expansion area was 331.02km and the 2 annual average expansion intensity was close to 10%. The study area was in the phase of high-speed expansion from 1990 to 2002. The high-speed expansion of land use for urban construction in the study area imposed great pressure on agricultural ecoenvironment. It became one of the important factors of the risk and pressure of agricultural eco-environment. The prediction of land use for urban construction would provide scientific and effective theoretical basis for the risk and pressure assessment on agricultural land use in the study area.

n t i
By building CEM (City Expanding Model in Metropolitan Area) model of the entire Selangor State, namely, city expansion in metropolitan area, the city expansion was predicted in Selangor in this study. An expanded model of the CA model, CEM model was proposed by He Chunyang et al. which combined the system dynamics with the traditional CA model to simulate city expansion in metropolitan area. Considering the relevant image data, spatial data and attribute data of Selangor, Malaysia and the capital Kuala Lumpur, CEM model was set to predict the city expansion of Selangor (Chunyang He, 2008;[START_REF] Shi Xiaoxia | Analysis and Prediction of Urban Expansion in Kuala Lumpur Based on Remote Sensing and CA Model [J[END_REF]. The city expansion of Selangor in 2020 was predicted with the residential map layer of Selangor in 2002 as the original map layer. The predicted results are shown in Fig. 1.

SERA Model

The pressure of city expansion on regional eco-environment is reflected by SERA model. The remote sensing model of the spatial ecological risk assessment, namely, SERA model, in this study is the assessment model based on landscape units ( x , y ). The ecological risk and pressure P on meso-scale landscape units ( x , y ) is reflected by the interaction of , the spatial driving force of the landscape unit, , the spatial influencing factor of macro-scopical region driving force, , the

E RI R AI A      . ( 3
)
A P , the spatial driving force of the landscape unit, includes the interrelations of DR , the relations between the landscape unit and the nearest road, DG , the relations between the landscape unit and the geometrical gravity centre of main plaque of construction land, , the relations between the landscape unit and construction land, and , the relations between the landscape unit and the spatial

FL DW heat environment. DR FL SA
and DG the influence of distance and co-react in spatial landscape drive, and and , the relationship of distance and area and coreact; includes , the patch size index of city construction land in the study area from a macro-perspective and , the patch shape index of city construction land in the study area. includes , the patch density index in the landscape unit, namely, its own stability of the landscape unit and , the slope; , the stability of neighborhood of a landscape unit.

is determined mainly by , the Accordingly, the remote sensing assessment model of ecological risk and pressure on the landscape was described as formula (4): (1) It could be seen from Fig. 2 and Table 2 that ecological risk grade of the cultivated land in the study area in 1990 was obviously low and was mainly primary risk without the occurrence of tertiary risk area. The secondary risk dominated in 2002 with the occurrence of tertiary or higher risk areas in different regions. The risk value of cultivated land and the risk grade were ever-increasing.

DW

  0 0 P P   1 1 1 1 0 ( ) 1 1 1 1 1 ( ) ( ) ( ) ( ) 2 1 1 1 1 ( ( ) ( ) 2 2 2 2 1 ( ) 2 1 1 ( ) ) ( ) 3
                                                           . ( 4
(2) It could be seen from Table 3 that the pixel number in remote sensing images of city construction land in the study area increased from 775,368 in 1990 to 1,522,789 in 2002. Its percentage increased from 8.49% to 16.67%. Meanwhile, cultivated land was ever-shrinking. The pixel number decreased from 4,763,258 in 1990 to 4,103,960 in 2002. Its percentage reduced by 7.22%. (3) Through the prediction results of city expansion, it could be known that there would be a further expansion of city construction land by 2020. With the constant expansion of city construction land, agricultural land would be occupied continuously, which led to the constant decrease of cultivated land. The agricultural land in study area would be threatened seriously both on its quantity and eco-environment.

  the total area of the spatial unit i (Liu Shenghe, 2000).

Fig. 1 .

 1 Fig. 1. Land use situation of Selangor State for city building and the predicted results in 2020.

  on the landscape unit ( x , y ), , the spatial influencing factor of macro-scopical regional resistance, , the characterization factor of RI P E P ecological risk based on the remote sensing of landscape unit ( x , y ), , the SI P comprehensive influencing factor of the regional ecological risk assessment, and 0 P , the adjustment coefficient of model. The ecological risk and pressure P is described as in formula (

  fragmentation of ecological landscape. P E is mainly reflected as the eco-environment index based on remote sensing. The comprehensive influencing factor of the model is reflected as UZ , the pressure on eco-environment imposed by human activities as well as lot of waste produced with the growth of economy.

  of Agro-Ecological Risk in the Study Area Based on the theory of the CA model and the landscape ecology, each factor in the model were realized concretely by combining with the actual situation and data in the study area. Based on SERA model and with the landscape unit area of 300m×300m the spatial ecological risk of the study area in 1990, 1991, 1994, 1996, 1998 and 2002 was assessed. The results range was 0~1 and was classified into 5 grades according to 0~0.2, 0.2~0.4, 0.4~0.6, 0.6~0.8 and 0.8~1. Fig.2 (a) and (b) refers to spatial risk assessment results of cultivated land in the study area in 1990 and 2002 respectively. Table2 refers to the ecological risk grade variation of the cultivated land in the study area in 1990, 1994 and 2002.

Fig. 2 .

 2 Fig. 2. The ecological risk variation in the study area in 1990 and 2002

Table 1 .

 1 City expansion intensity in the study area from 1990 to 2002

	Expansion area (km 2 )	1990-1994 153.41	1994-1998 331.02	1998-2002 188.26
	Annual average expansion	5.50	9.72	3.98
	intensity (%) Annual expansion area (km 2 )	38.35	82.75	47.06
	Expansion type	High-speed	High-speed	High-speed

  [START_REF] Shi Dengrong | Comparative Study on Air Quality of Several Cities in the Yangtze River Delta[END_REF]

	A AI P P P P P P P R RI SI
			n				n			n			n
	n	Ai	i	Ai P	n	AIj	j	AIj P	Ri n	i	Ri P	RIj n	j	RIj P	E P	SI
				DR DG			FL DW		SA SF
															UZ P
			SD SI NS FN	LA CW

Table 2 .

 2 Ecological risk grade variation of the cultivated land in the study area (the number of landscape units)

	Year	Total cultivated land	Primary risk	Percentage (%)	Secondary risk	Percentage (%)	Tertiary risk	Percentage (%)
	1990	4805161	4636729	96.49	168432	96.49	0	0
	1994	4491520	2894320	64.44	1575904	64.44	21296	0.47
	2002	4030752	288464	7.16	3546752	7.16	195536	4.85

Table 3 .

 3 Variation of residential land and cultivated land from 1990 to 2002

		Residential land	Cultivated land
	Year	Area (pixel number)	Percentage (%)	Area (pixel number )	Percentage (% )
	1990	775368	8.49	4763258	52.15
	1991	1073515	11.75	4326125	47.36
	1994	945818	10.36	4505771	49.33
	1998	1313614	14.38	4365608	47.80
	2002	1522789	16.67	4103960	44.93