Abstract
A simple technique for prediction of suspended sediment concentration (SSC) or total suspended matter in the Himalayan Bhagirathi river is presented. Artificial neural network models have been developed using short time period data of discharge (
Q
) and SSC during the high activity monsoon period of June to October 2004, when variations are maximum. Two modeling approaches have been employed, a daily approach and a three hourly approach. Although the time period considered is the same in both the approaches, the modeling performance is marginally better in the three hourly approaches where there is a sixfold increase in the data set. The Levenberg–Marquardt optimization algorithm has been improved with NARX [nonlinear autoregressive with exogenous input] architecture and high values of coefficient of determination have been obtained [0.89–0.97]. This study shows that short duration time series data can be used for successfully predicting geo-hydrological variables in the highly complex Himalayan river scenario.