Prediction of Suspended Sediments in a Hyper – Concentrated River Using Neural Networks

Prediction of suspended sediment concentration in hyper-concentrated rivers is a crucial task in modeling and designing of hydraulic structures such as dams, reservoirs, barrages and water intake inlets. In this study, suspended sediment concentration in Kinta River has been predicted using radial basis function (RBF) neural network modeling technique. Time series of suspended sediments and stream discharge data from 1992 to 1995 are used in the training and testing stages of the model. The data were divided into two sections based on the model stages as training and testing. The Thin Plate Spline (TPS) basis function was used to establish TPS - RBF prediction model. The input neurons were selected based on previous studies about the suspended sediment prediction models. The number of hidden neurons was determined by trial and error method. The spread of the basis function was determined by normalization method. The performance of the prediction model was evaluated using three statistical performance measures namely root mean square error (RMSE), coefficient of efficiency (CE) and coefficient of determination (R2). The results showed that the TPS – RBF model predicted the suspended sediment values close to the observed data. The statistics of the model showed that the prediction model performed very well and produced R2 values close to one in both training and testing stages.

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