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
A split-step PSO Algorithm in predicting construction litigation outcome
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
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. In this paper, neural networks are used to predict 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. The network is trained by using two different algorithms. It is demonstrated that the artificial neural network approach, which is able to provide model-free estimates in deducing the output from the input, is an appropriate forewarning tool. It is shown from the training and verification simulation that the water stage prediction results are highly accurate and are obtained in very short computational time. Both these two factors are important in water resources management. Besides, sensitivity analysis is carried out to evaluate the most suitable network characteristics including number of input neurons, number of hidden layers, number of neurons in hidden layer, number of output neurons, learning rate, momentum factor, activation function, number of training epoch, termination criterion, etc. under this specific circumstance. The findings lead to the reduction of any redundant data collection as well as the accomplishment of cost-effectiveness.