Estimation of suspended sediment load using three neural network algorithms in Ramganga River catchment of Ganga Basin, India

Abstract

The information on suspended sediments of river is considered to be crucial for issues concerning water management and the environment. The abrupt quantity and nature of sediment loads can be best studied by simultaneously considering the governing variables contributing towards this physical phenomenon. Artificial Neural Network (ANN) is one of the suitable data-mining technique which helps in carrying out the modelling of this phenomenon. In this study, ANNs are employed to approximate the monthly mean suspended sediment load for Ramganga River. Three simulations with rainfall and water discharge data were carried out to predict the suspended sediment load. In terms of the selected performance criteria, three algorithms were evaluated and the results so obtained are presented. It has been found that rainfall values were not sufficient to correctly predict the suspended sediment load. However, considering water discharge values as input improves the performance of all the three considered algorithms.