Real-Time Prediction of Water Stage with Artificial Neural Network Approach
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Calibration of Flow and Water Quality Modeling Using Genetic Algorithm
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
River stage forecasting with particle swarm optimization
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Long-Term prediction of discharges in manwan reservoir using artificial neural network models
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
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
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Precise flood forecasting is desirable so as to have more lead time for taking appropriate prevention measures as well as evacuation actions. Although conceptual prediction models have apparent advantages in assisting physical understandings of the hydrological process, the spatial and temporal variability of characteristics of watershed and the number of variables involved in the modeling of the physical processes render them difficult to be manipulated other than by specialists. In this study, two hybrid models, namely, based on genetic algorithm-based artificial neural network and adaptive-network-based fuzzy inference system algorithms, are employed for flood forecasting in a channel reach of the Yangtze River. The new contributions made by this paper are the application of these two algorithms on flood forecasting problems in real prototype cases and the comparison of their performances with a benchmarking linear regression model in this field. It is found that these hybrid algorithms with a “black-box” approach are worthy tools since they not only explore a new solution approach but also demonstrate good accuracy performance.