• Dr.C.S.V.Subrahmanya Kumar
• Dr.G.K.Viswanadh

The assessment of the volume of sediment transported by a river is of vital interest in hydraulic engineering. Neural Network model is formulated to predict daily suspended sediment yield in river Godavari at Perur using Gradient Descent back propagation algorithm. To test the robustness of the model formulated, statistical performance indices namely, Correlation coefficient (R), Average Absolute Relative Error (AARE), Nash coefficient of efficiency (η) and Normalized Root Mean Square Error (NRMSE) are computed during training and testing. The results indicate that ANN can be effectively used to model daily suspended sediment yield.

Sediment yield, Artificial Neural Networks, back propagation algorithm

"Development of Sediment Transport Model using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 3, page no.274-280, March-2018, Available :http://www.jetir.org/papers/JETIR1803053.pdf

"Development of Sediment Transport Model using Artificial Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 3, page no. pp274-280, March-2018, Available at : http://www.jetir.org/papers/JETIR1803053.pdf