River Stage Forecasting with Particle Swarm Optimization

Abstract

An accurate water stage prediction allows the pertinent authority to issue a forewarning of the impending flood and to implement early evacuation measures when required. Existing methods including rainfall-runoff modeling or statistical techniques entail exogenous input together with a number of assumptions. The use of artificial neural networks has been shown to be a cost-effective technique. But their training, usually with back-propagation algorithm or other gradient algorithms, is featured with certain drawbacks, such as very slow convergence and easily getting stuck in a local minimum. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting real-time water levels in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or stage/time history at the specific station. It is shown from the verification simulations that faster and more accurate results can be acquired.