Problem solving of uncertainty and independence factors in Agricultural Capability Evaluation by Using ANP FUZZY Method

Introduction:
Agriculture is one of the main uses of the land. Using agriculture to increase production is always with acidification and loss of groundwater reserves. Therefore, with proper planning for use of agricultural land so that to leave the least negative impacts on the soil and water pollution, it is regarded imperative for sustainable development. This study is aimed to better understanding of the environmental assessment of the area to be examined in the Birjand agricultural water reserves and to assess the agriculture. One of the problems that exist in the common assessment methods is the lack of precision of space, as well as the assumption of independent valuation factors and not seeing the relationships and feedbacks in the evaluation process. In the current research with the aim of resolving the problem to determine a more accurate assessment of using agricultural land according to environmental factors and the underlying factors of the “Fuzzy” method and techniques using a network analysis process.
Materials and Methods:
- Description of study area:
The research study is the Birjand's Watershed. That it is located in the East of Iran and the County of Southern Khorasan and the City of Birjand is at the center of there. The scope of the geographical location is located at 41, 58 and 44, 59 E and 44, 32 to 8, 33 N latitude. The Birjand plain has an average annual rainfall of 140 mm and an average temperature of 16.5 degrees Celsius, according to the climate classification it is one of the most arid areas.
-Method:
-The use of the “Fuzzy” logic in the Evaluation of land use
In 1996 Zou and Sivico, found some of the problems associated with the implementation of multi-criteria assessments and GIS, these states are: Input for multi-criteria assessment methods GIS usually are ambiguous, inaccurate and wrong. Despite this awareness of these methods assuming that the input data is accurate. In this connection, some efforts in connecting to this problem by combining multiple criteria techniques in GIS was conducted, and the analysis of the sensitivity and the propagation were performed. Another way to deal with uncertainty of input data (values and priorities of decision-makers) was to use the “Fuzzy” logic approach.
- Weighting by Analytic Network Process (ANP)
In this study, based on various factors and the intrinsic nature of space problems, using the ANP method. ANP method is one of the techniques of multiple attribute decision making (MADM). The ANP method is a developed method of AHP, which can be correlation and give a feedback between effective elements in the decision making and modeling, and all internal effects of effective components in decision-making, are used and entered into the account. The ANP technique with a comprehensive framework, with all interactions and relationships, between decision-making levels are the formation of a network structure, can be considered. Clusters represent levels of making decisions and arrows indicate interactions between the decision making levels. The direction is determined by the dependence arcs. In some cases where the elements of a cluster of or all the elements affect other clusters (or are influenced by it), communicate between clusters that are called external dependence.
Results:
According to the issue mentioned it will be decided by a structured value tree. The framework, where the measurement to achieve the objectives set are introduced and presented.
-Preparation of “Fuzzy” Maps for each factor
At this stage, for each of the factors identified, in the previous stage a map was provided based on the utility of the aim (areas suitable for agriculture) to study. For drawing up the maps, GIS and Idrisi Selva Software is used.
-The Prohibition Layers: protection land use
Areas with slopes above 70%, protected areas of the Department of Environment and flood-prone areas (Figure) capable for protection, so that the area is not to be used by anyone and they should be removed from the research area. Determining prohibitions and standardization are based on Boolean Logic (0 and 1).
- Weighing the Factors by using the ANP Method
According to factors affecting at land capability of agricultural development is identification, and was drown in the software. Then the software gives us the final weight (as below). All of these steps use the Super Decision Software 2.0.8.version.
climate:0.148,rainfall: 0.046, Evaporation:0.015, slop: 0.066.distance of river:0.0149, distance of water resources:0.022, texture soil:0.058, Soil fertility:0.073, Soil drainage: 0.028, Depth soil: 0.045, soil erosion:0.074, land cover: 0.406
Linear Combination:
At this stage, the layers of the raster weight are based on gathering the formula (1) to obtain the final Fitness map. In this relation: S is fit Land, Wi is operating weight, Xi is the operating phase, and Ci is rating the prohibited criteria.
Formula(1) S=∑_(i=1 )^n▒W_i X_i ∏_ ^ ▒C_i
Comparing the Method of Evaluation of the Iranian Model with the ANP FUZZY Technique
From the following forms, the area assigned to each class for two methods can be stated as follows:








Pic1. compares the results of evaluation, using the ANP FUZZY Method and the Iranian Model (overlay)

Table (1) compares the results of evaluation, using the ANP FUZZY Method and the Iranian Model (overlay)
Area
overlay
(m2) quota overlay
(%) Area
ANP FUZZY
(m2) quota
ANP FUZZY
(%)
Good capability class 110108345 3.21 271674384 7.93
Moderate capability class 150745348 4.40 707816248 20.66
less capability class 885840235 25.86 986628010 28.80
no capability class 2278320097 66.52 1458895384 42.59

Discussion and Conclusion:
In this study, at first, based on 12 environmental and infrastructure factors, and also considering restrictions, the value of each pixel of the study area, the ANP FUZZY technique has been achieved in agriculture. In drawing up the map, we tried a few things, to be observed: 1- Comprehensive measures to be selected for the proper evaluation. 2- The entering of uncertainties and standardization of all factors carefully raised the ranking. 3- By using the Analytic network process, incorrect assumption of independence, has been removed, the feedback and interaction are considered in the assessment. Firstly, the map of multi-criteria evaluation is to determine the suitability of the land, suitable areas for agriculture in the study area, is a consolidated map with a raster format and is for areas that do not have a limit of development, and that the amount has values from 0 to 203. There are not areas with a higher value of 203 in this area and this shows that the areas with great potential for agriculture are not available in the area. More utility indicates the higher capability, and the less utility indicates lower capability than for the corresponding user. The utility of each pixel represents the favorable factors and the weights assigned to them. According to this method, it was revealed that 93/7%, 66/20%, 80/28%, and 50/42% of the study areas respectively, have a high, medium, low and unable capability to make them useable for agricultural. So, finally, by comparing the two methods of the Iranian Model and the ANP FUZZY Technique, the conclusion is that the Iranian Method has been a simpler approach to nature and its interactions, in the present method, have a more comprehensive identification of impact factors, entering uncertainty of the “Fuzzy” Technique and also see interactive network analysis techniques to design more realistic and arriving at a more detailed process which is closer to the situation of villages.
Keywords: Agriculture capability evaluation, Analytic Network Process (ANP), fuzzy, protection, Birjand's Watershed.