Using Fuzzy Logic-Based Modeling to Improve the Performance of the Revised Universal Soil Loss Equation

*L.T. Tran, M.A. Ridgley, and R. Sutherland, University of Hawaii, Geography Department, 2424 Maile Way, Honolulu, HI 96822, USA; M.A. Nearing, National Soil Erosion Laboratory, USDA-ARS, West Lafayette, IN 47907-1196, USA; L. Duckstein, Ecole Nationale du Genie Rural, des Eaux et des Forets, 19 avenue du Maine, 75732 Paris Cedex 15, France. *Corresponding author: [email protected]. Current address: Center for Integrated Regional Assessment, Penn. State University; 2217 Earth & Engineering Science Building, University Park, PA 16802, USA. ABSTRACT This paper reports the application of fuzzy logicbased modeling (FLBM) to improve the performance of the Revised Universal Soil Loss Equation (RUSLE). The FLBM approach was to make the RUSLE’s structure more flexible in describing the relationship between soil erosion and RUSLE factors and in dealing with data and model uncertainties while not requiring any further information. The approach used in this study consists of two techniques: multiobjective fuzzy regression (MOFR) and fuzzy rule-based modeling (FRBM). First, MOFR was used to derive the relationship between soil loss and a combination of RUSLE factors. These MOFR models were in turn linked together in a FRBM framework. Then these fuzzy rules were applied to adjust the RUSLE prediction corresponding to each combination of RUSLE factors. The Nash-Sutcliffe model efficiency of the fuzzy model on a yearly basis was 0.70 while RUSLE's was 0.58. On an average annual basis, the efficiency was 0.90 and 0.72 for the fuzzy model and RUSLE, respectively. With several good characteristics, the FLBM approach can be used to improve the performance of RUSLE with little effort and modification to the existing RUSLE model.