Journal of Watershed Management Research

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     Prediction of flood peak discharge and runoff volume is one of the major challenges in the management of watersheds. The present study was carried out to estimate event flood peak discharge and runoff volume using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in Kasilian watershed, Iran. For this purpose, 15 rainfall characteristics were considered for 60 storms from 1975 to 2009. Statistical indices of mean square error (RMSE), coefficient of efficiency (CE) and the coefficient of determination (R²) were used to assess models performance. The results showed that flood peak discharge variable, ANFIS with RMSE=1.28m³s^-1, CE=%82 and  R²=0.86 has better performance than ANN with RMSE=1.22m³s^-1, CE=%82 and  R²=0.95 and for runoff volume variable, ANFIS with RMSE=2369.54 m³, CE=%99 and  R²=0.99 has better performance than ANN with RMSE=10282.82m³, CE=%98 and  R²=0.98. Also, the results of the sensitivity analysis indicated that the most sensitive factor is excess rainfall for runoff flood peak discharge and runoff volume estimation.

Keywords: ANN, ANFIS, Excess rainfall, Factor analysis, Kasilian watershed, Sensitivity analysis

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