Capability assessment of conventional and data-driven models for prediction of suspended sediment load.

Abstract

Information about suspended sediment concentration (SSC) in the stream is vital for sustainability of water conservation and erosion control planning, designing and monitoring. In this research, prediction of SSC has been done using artificial neural network (ANN), support vector regression (SVR) and multi-linear regression (MLR) models. Performance evaluation of developed models has been carried out on the basis of root mean square error (RMSE), correlation coefficient (r), coefficient of efficiency (CE) and pooled average relative error (PARE). Cross-correlation function (CCF) validated that gamma test (GT) is an appropriate tool for the selection of most responsive input variables. On the basis of GT and CCF, GT-6 model was selected as the model with most effective input variables, with the values of gamma, standard error and V-ratio as 0.0643, 0.00583 and 0.2570, respectively. The ANN (6-3-1) model performed better than the other single and double hidden layered ANN models with the values of r, RMSE, CE and PARE as 0.939, 0.0063g/l, 85.17 and 0.0160, respectively. The performance of the SVR model was found better with the values of r, RMSE, CE and PARE as 0.906, 0.018g/l, 79.09 and 0001, respectively, but slightly poor than the selected ANN (6-3-1) model. The values of r, RMSE, CE and PARE were found as 0.899, 0.0312g/l, 65.15 and - 0.0031, respectively, in the case of MLR model. The present study revealed that among the ANN, SVR and MLR models, the ANN model with a single hidden layer is most suitable for observed SSC. The present study offers the simple efficient model to estimate the suspended sediment concentration in the stream with minimum error using limited data set.