Neural networks approached for modelling river suspended sediment concentration due to tropical storms

Artificial neural networks are one of the advanced technologies employed in hydrology
modelling. This paper investigates the potential of two algorithm networks, the feed forward
backpropagation (BP) and generalized regression neural network (GRNN) in comparison with
the classical regression for modelling the event-based suspended sediment concentration at
Jiasian diversion weir in Southern Taiwan. For this study, the hourly time series data
comprised of water discharge, turbidity and suspended sediment concentration during the
storm events in the year of 2002 are taken into account in the models. The statistical
performances comparison showed that both BP and GRNN are superior to the classical
regression in the weir sediment modelling. Additionally, the turbidity was found to be a
dominant input variable over the water discharge for suspended sediment concentration
estimation. Statistically, both neural network models can be successfully applied for the
event-based suspended sediment concentration modelling in the weir studied herein when
few data are available.