Predicting construction litigation outcome using particle swarm optimization
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Reliability and performance-based design by artificial neural network
Advances in Engineering Software
Prediction of construction litigation outcome using a split-step PSO algorithm
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Algal bloom prediction with particle swarm optimization algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Evaluation of several algorithms in forecasting flood
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
A split-step PSO algorithm in prediction of water quality pollution
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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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.