Development of an intelligent system based on ANFIS model for predicting soil erosion

Abstract

This study explores capability of adaptive neuro-fuzzy interface system (ANFIS) model for soil erosion assessment of which various parameters are involved and the parameters interaction is highly nonlinear. Soft computing technique has been applied widely in various fields, but its application in soil science is limited. A number of models have been established in predicting soil erosion such as a revised universal soil loss equation (RUSLE) model whereby parameters of the RUSLE model are employed in testing the ANFIS model in this study. Besides, a new parameter of biomass microbe (Mf) is introduced and its effectiveness in controlling erosion is demonstrated. A hilly area near the Guthrie Corridor Expressway, Malaysia, is chosen as a study area whereby five experimental plots size of 8 × 8 m and 5 × 5 m are developed. These five plots are characterized by either natural or planted vegetation or either with or without microbe bio-fertilizer. Input parameters of RUSLE, i.e. rainfall erosivity, soil erodibility, slope length and steepness, vegetation and support practice, are measured, and observed erosion is monitored for 1 year. The microbe factor presented as Mf is examined by pairing the plot with and without microbe while keeping other similar characteristic for those plots. Result shows a satisfactory achievement between observed and predicted model by ANFIS with R2, RMSE and MAE being 0.8275, 1.4276 and − 0.0165, respectively. In terms of a relationship between the Mf factor and soil erosion, it is noticed that the soil erosion is inversely proportional to the Mf when the entire erosion conservation practices are unchanged for a given period. Therefore, the application of microbes could be an alternative or additional erosion conservation technique in soil conservation works.