Welcome to Eprints@IARI

Abstract

Two Artificial Neural Network (ANN) models, one geomorphology-based (GANN) and another non-geomorphology-based (NGANN) for the prediction of sediment yield were developed and validated using the hydrographs and silt load data of 1995–1998 for the Banha watershed in the Upper Damodar Valley in Jharkhand state in India. The sediment loads predicted by these models were compared with those predicted by an earlier developed regression model for the same watershed. It was revealed that the feed-forward ANN model with back propagation algorithm performed well for both the GANN and NGANN models. However, the GANN predicted better with highest coefficient of determination (R2) of 0.98, model efficiency (E) of 0.96 and absolute average deviation (AAD) of 0.0017 in comparison to NGANN (R2 = 0.94, E = 0.81, AAD = 0.006). The regression model performance was inferior (R2 = 0.940.78, E = 0.72, AAD = 0.023) to the ANN models. The Neural- work-ProII-plus and MATLAB software were used for development of the ANN models. It was also revealed that association of geomorphological parameters viz. relief factor, form factor and drainage factor with runoff rate resulted in a better prediction of sediment loss.