Forecasting Runoff and Sediment Yield by ANN and Fuzzy Logic Algorithms for Kal River, India

The ANN and fuzzy logic (FL) models were developed to forecast the runoff and sediment yield for catchment of Kal River, India in METLAB 2.9b witting the programme supporting to nntool. The input to the models were used as daily rainfall, evaporation, temperature and one day and tow day lag runoff for runoff modelling. Whereas, for sediment yield modelling inputs in ANN and Fuzzy logic model used as daily rainfall, one and two day runoff. The inputs data for both models of 21 years (1991 to 2011) were considered in present study on daily basis. The 14 years (1991 to 2004) used in developing the models whereas rest 7 years (2005 to 2011) for validation of the models. In sediment yield modelling, 7 years (2003 to 2009) data were used for developing and validation of models. The models performance were evaluated by standard statistical indices such R, RMSE, EV, CE, and MAD. It was found that ANN model performance improved with increasing the input vectors. The fuzzy logic model was performed well with R value more than 0.95 during developmental stage and validation stage over ANN model for predicting runoff and sediment yield. Hence, FL model found to be more superior to ANN in prediction of runoff and sediment yield for Kal river.