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United States Department of Agriculture

Agricultural Research Service

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

item Tran, L - UNIV. OF HAWAII
item Nearing, Mark
item Duckstein, L - ECOLE NATIONAL, FRANCE
item Ridgley, M - UNIV OF HAWAII
item Sutherland, P - UNIV. OF HAWAII

Submitted to: International Soil Conservation Organization Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: May 23, 1999
Publication Date: N/A

Technical Abstract: This paper reports the application of fuzzy logic-based modeling to improve the performance of the Revised Universal Soil Loss Equation (RUSLE). The aim of the fuzzy logic-based modeling 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 fuzzy modeling approach used in this study consists of 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, using the same inputs as the RUSLE, we applied the fuzzy rule set to adjust the RUSLE-derived soil-erosion prediction corresponding to each combination of RUSLE factors. The Nash-Sutcliffe model efficiency of the fuzzy model, on a yearly basis, was 0.67, 0.75, and 0.70 for calibration, validation, and whole data sets, respectively, while the RUSLE's was 0.58 for the whole data set. On an average annual basis, the efficiency was 0.90 and 0.72 for the fuzzy model and the RUSLE, respectively. As the approach is quite simple, no other data outside the RUSLE are needed, and the main structure of the RUSLE is maintained, the fuzzy model in this study can be used to considerably improve the performance of the RUSLE with little effort and modification to the existing RUSLE model.

Last Modified: 4/22/2015
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