ANN Based Sediment Prediction Model Utilizing Different Input Scenarios

Modeling sediment load is a significant factor in water resources engineering as it affects directly the design and management of water resources. In this study, artificial neural networks (ANNs) are employed to estimate the daily sediment load. Two different ANN algorithms, the feed forward neural network (FFNN) and radial basis function (RBF) have been used for this purpose. The neural networks are trained and tested using daily sediment and flow data from Rantau Panjang station on Johor River. The results show that combining flow data with sediment load data gives an accurate model to predict sediment load. The comparison of the results indicate that the FFNN model has superior performance than the RB model in estimating daily sediment load.