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| Titel |
Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites |
| VerfasserIn |
Yen-Ming Chiang, Li-Chiu Chang, Meng-Jung Tsai, Yi-Fung Wang, Fi-John Chang |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 14, no. 7 ; Nr. 14, no. 7 (2010-07-16), S.1309-1319 |
| Datensatznummer |
250012371
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| Publikation (Nr.) |
 copernicus.org/hess-14-1309-2010.pdf |
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| Zusammenfassung |
| In this research, we propose recurrent neural networks (RNNs) to build a
relationship between rainfalls and water level patterns of an urban sewerage
system based on historical torrential rain/storm events. The RNN allows
signals to propagate in both forward and backward directions, which offers
the network dynamic memories. Besides, the information at the current
time-step with a feedback operation can yield a time-delay unit that
provides internal input information at the next time-step to effectively
deal with time-varying systems. The RNN is implemented at both gauged and
ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level
predictions. The results show that the RNN is capable of learning the
nonlinear sewerage system and producing satisfactory predictions at the
gauged sites. Concerning the ungauged sites, there are no historical data of
water level to support prediction. In order to overcome such problem, a set
of synthetic data, generated from a storm water management model (SWMM)
under cautious verification process of applicability based on the data from
nearby gauging stations, are introduced as the learning target to the
training procedure of the RNN and moreover evaluating the performance of the
RNN at the ungauged sites. The results demonstrate that the potential role
of the SWMM coupled with nearby rainfall and water level information can be
of great use in enhancing the capability of the RNN at the ungauged sites.
Hence we can conclude that the RNN is an effective and suitable model for
successfully predicting the water levels at both gauged and ungauged sites
in urban sewerage systems. |
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