Artificial Neural Networks Modeling for Suspended sediment yield estimation over Kshipra Catchment, Madhya Pradesh

Abstract

Artificial Neural Network is a vigorous technique to develop massive relationship between the input and output variables, and able to remove complex behavior between the water resources variables such as river sediment and discharge. AAN were developed, to predict sediment yield on a daily basis for monsoon period. Model performance has been evaluated in terms of Correlation coefficient (R), Mean squared error (MSE), Root mean squared error Ratio (RMSR) and Nash–Sutcliffe model efficiency (NSE). The basic ANN architecture was optimized in term of training algorithm, number of neurons in the hidden layer, input variables for training of the model. Twelve algorithms for training the neural network have been evaluated. Performance of the model was evaluated with number of neurons varied from 1 to 25 in the hidden layer. It was observed that predicted sediment yield better correlated to observed sediment yield (R=0. 9933 and 0.9567)