Monitoring event-based suspended sediment concentration by artificial neural network models

Authors:
Yu-Min Wang;Seydou Traore;Tienfuan Kerh
Affiliations:
Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan;Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Pingtung, Taiwan;Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, Taiwan
Venue:
WSEAS Transactions on Computers
Year:
2008

Citing 2
Cited 4

Artificial Neural Networks in Hydrology

Artificial Neural Networks in Hydrology
Generalized regression neural network in modelling river sediment yield

Advances in Engineering Software

Neural network approach for estimating reference evapotranspiration from limited climatic data in Burkina Faso

WSEAS Transactions on Computers
Automatic sea floor characterization based on underwater acoustic image processing

WSEAS Transactions on Signal Processing
Research on the evaluation methods of bid of construction project based on improved BP neural network

WSEAS Transactions on Computers
Municipal revenue prediction by ensembles of neural networks and support vector machines

WSEAS Transactions on Computers

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper is concerned with monitoring the hourly event-based river suspended sediment concentration (SSC) due to storms in Jiasian diversion weir in southern Taiwan. The weir is built for supplying 0.3 million tons of water per day averagely for civil and industrial use. Information of suspended sediments fluxes of rivers is crucial for monitoring the quality of water. The issue of water quality is of particular importance to Jiasian area where there are high population densities and intensive agricultural activities. Therefore, this study explores the potential of using artificial neural networks (ANNs) for modeling the event-based SSC for continuous monitoring of the river water quality. The data collected include the hourly water discharge, turbidity and SSC during the storm events. The feed forward backpropagation network (BP), generalized regression neural network (GRNN), and classical regression were employed to test their performances. From the statistical evaluation, it has been found that the performance of BP was slightly better than GRNN model. In addition, the classical regression performance was inferior to ANNs. Statistically, it appeared that both BP (r2=0.930) and GRNN (r2=0.927) models fit well for estimating the event-based SSC in the Jiasian diversion weir. The weir SSC estimation using a single input data with the neural networks showed the dominance of the turbidity variable over water discharge. Furthermore, using the the ANN models are more reliable than classical regression method for estimating the SSC in the area studied herein.