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Title: Evaluation of several algorithms in forecasting flood
Authors: Wu, CL
Chau, KW 
Keywords: Benchmarking
Genetic algorithms
Neural networks
Issue Date: 2006
Publisher: Springer-Verlag
Source: In M Ali & R Dapoigny (Eds.), Advances in applied artificial intelligence : 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, June 27-30, 2006 : proceedings, p. 111-116. Berlin: Springer-Verlag, 2006 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 4031
Abstract: 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.
ISBN: 978-3-540-35453-6
DOI: 10.1007/11779568_14
Rights: © Springer-Verlag Berlin Heidelberg 2006. The original publication is available at
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