Predict Soil Erosion with Artificial Neural Network in Tanakami (japan)

In recent years using artificial neural networks has increased as powerful tool with capability to predict linear and nonlinear relationships in complex engineering problems. Using this toolbox has been significant in different civil engineering fields, especially hydrological problems for various important parameters with different variables and complex mathematical equation. Predict soil erosion has been studied as one of the important parameters of the catchment management in this study. To obtain data artificial rainfall was used in a catchment located in Jakujo Rachidani in Tanakami area. Artificial network has developed foe predict soil erosion and this results compared with obtained results from Multi Linear Regression (MLR) . The results show high ability of ANN to Prediction of soil erosion compared to MLR. The performance of each model is evaluated using the Mean Square Error (MSE), Root Mean Square Error (RMSE) Correlation Coefficient(R), Correlation of determination (R 2 ), Mean Absolute Relative Error (MARE). Key-Words: Soil Erosion, Catchment, Modeling, Artificial neural network, Multi linear regression, Mean Square Error