Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1273
Title: Evaluation of several algorithms in forecasting flood
Authors: Wu, CL
Chau, KW 
Keywords: Benchmarking
Floods
Genetic algorithms
Hydrology
Neural networks
Rivers
Watersheds
Issue Date: 2006
Publisher: Springer Berlin / Heidelberg
Source: Lecture notes in artificial intelligence, 2006, v. 4031, p. 111-116 How to cite?
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.
URI: http://hdl.handle.net/10397/1273
ISBN: 978-3-540-35453-6
DOI: 10.1007/11779568_14
Rights: © Springer-Verlag Berlin Heidelberg 2006. The original publication is available at http://www.springerlink.com.
Appears in Collections:Book/Book Chapter

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