Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/1134
| Title: | A flood forecasting neural network model with genetic algorithm | Authors: | Wu, CL Chau, KW |
Issue Date: | 2006 | Source: | International journal of environment and pollution, 2006, v. 28, no. 3/4, p. 261-273 | Abstract: | It will be useful to attain a quick and accurate flood forecasting, particularly in a flood-prone region. The accomplishment of this objective can have far reaching significance by extending the lead time for issuing disaster warnings and furnishing ample time for citizens in vulnerable areas to take appropriate action, such as evacuation. In this paper, a novel hybrid model based on recent artificial intelligence technology, namely, a genetic algorithm (GA)-based artificial neural network (ANN), is employed for flood forecasting. As a case study, the model is applied to a prototype channel reach of the Yangtze River in China. Water levels at downstream station, Han-Kou, are forecasted on the basis of water levels with lead times at the upstream station, Luo-Shan. An empirical linear regression model, a conventional ANN model and a GA model are used as the benchmarks for comparison of performances. The results reveal that the hybrid GA-based ANN algorithm, under cautious treatment to avoid overfitting, is able to produce better accuracy performance, although in expense of additional modeling parameters and possibly slightly longer computation time. | Keywords: | Flood forecasting model Hybrid algorithms Artificial neural networks Genetic algorithms |
Publisher: | Inderscience | Journal: | International journal of environment and pollution | ISSN: | 0957-4352 (print) 1741-5101 (online) |
DOI: | 10.1504/IJEP.2006.011211 | Rights: | Copyright © 2006 Inderscience Enterprises Ltd. The journal web page at: http://www.inderscience.com/browse/index.php?journalID=9 |
| Appears in Collections: | Journal/Magazine Article |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| IJEP4.pdf | Pre-published version | 218.28 kB | Adobe PDF | View/Open |
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