Using a fuzzy logic decision system to optimize the land suitability evaluation for a sprinkler irrigation method

Land evaluation for irrigation is the process of predicting land use potential on the basis of soil attributes. The Food and Agriculture Organization (FAO) framework for land suitability evaluation is the most commonly used and is based on the biophysical properties of lands. The FAO framework method for land suitability application Boolean approach that has been criticized by some researchers. Because the Boolean representations ignore the continuous nature of soil and uncertainties in measurement and also its inability for overcoming problems related to vagueness in definition and other uncertainties, fuzzy set methodologies have been proposed. In the present study, the qualitative land suitability evaluation for sprinkler irrigation using parametric-based FAO learning and fuzzy inference system was carried out in an area of 5175 ha in Northwest Iran. By overlaying the layers (soil texture, soil depth, lime, electric conductivity, drainage, and slope) and use of spatial data modeler in ArcGIS 9.3, land evaluation maps for sprinkler irrigation were provided for the area under study. Results showed that based on the parametric approach, 1598 ha of the study area were classified as highly suitable (S1 class) for sprinkler irrigation; the area of highly suitable lands in the parametric method was about five times the area of highly suitable lands obtained through the fuzzy method. In addition, the two methods were completely different in determining moderately suitable lands (S2). Accordingly, 787 ha in the area was moderately suitable using the parametric method, which was about two times that obtained through the fuzzy method. This showed the significant difference between two methods applied to evaluate the lands. Moreover, fuzzy approaches accommodate the continuous nature of some soil properties and produce more intuitive distributions of land suitability indexes.