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)
