Sediment yield prediction using neural networks at a watershed in south east India

Abstract

In this article, feed-forward backpropagation neural network (BPNN) and the radial basis neural network (RBNN) are used to predict daily sediment yield during monsoon. The performance of BPNN and RBNN models for sediment yield during July, August, and September over 16 year’s data, has been evaluated. It is found that in the small watershed of Lahunipada, India, BPNN performs best for estimating sediment yield. With BPNN, the coefficient of determination (R2) values for July, August, and September are found to be 0.9553, 0.9437, and 0.9864, respectively. Similarly, with RBNN the R2 values for July, August, and September are 0.9521, 0.9361, and 0.959, respectively. Over all, results show that with the same scenario, performance for July, August, and September agreed to RBNN with less coefficient of determination as compared to BPNN. Furthermore, BPNN model using sigmoid transfer function predicts the sediment yield within a range of 0.0219–1.54 t/ha during July, 0.013–1.778 t/ha during August and 0.001–1.389 t/ha during September and sheer deviations in RBNN with Gaussian transfer function. This study confirms that BPNN and RBNN are useful techniques for predicting sediment yield in small watersheds of south east India.