Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database
| Autor: | T.-Y. Pan, J.-S. Lai, T.-J. Chang, H.-K. Chang, K.-C. Chang, Y.-C. Tan |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: | |
| Zdroj: | Natural Hazards and Earth System Sciences, Vol 11, Iss 3, Pp 771-787 (2011) |
| Druh dokumentu: | article |
| ISSN: | 1561-8633 1684-9981 |
| DOI: | 10.5194/nhess-11-771-2011 |
| Popis: | This study attempts to achieve real-time rainfall-inundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations. |
| Databáze: | Directory of Open Access Journals |
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