Application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation

Abstract This paper discusses the application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 1700 plot-years of data, taken from more than 200 plots at 21 sites in the U.S., showed that soil erosion was not adequately described merely by the multiplication of five RUSLE factor values in all cases. The fuzzy logic-based modeling approach was to make the RUSLE's structure more flexible in describing the relationship between soil erosion and other factors and in dealing with data and model uncertainties without requiring any further information. The approach used in this study consisted of two techniques: multi-objective fuzzy regression (MOFR) and fuzzy rule-based modeling (FRBM). First, MOFR was applied to small subsets of RUSLE factor values to derive the relationship between soil loss and the rainfall erosivity factor within each subset of data. These MOFR models, considered as single fuzzy rules, were in turn linked together in a FRBM framework to form a fuzzy rule set. Then the fuzzy rule set was applied to compute the soil loss prediction corresponding to each combination of RUSLE factors. The model efficiency [Journal of Hydrology (Amsterdam) 10 (1970) 282] of the fuzzy model on a yearly basis was 0.70 while the RUSLE's was 0.58. On an average annual basis, the model efficiency was 0.90 and 0.72 for the fuzzy model and the RUSLE, respectively.

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