Abstract
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.
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