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| Titel |
Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks |
| VerfasserIn |
Y.-M. Chiang, L.-C. Chang, M.-J. Tsai, Y.-F. Wang, F.-J. 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 ; 15, no. 1 ; Nr. 15, no. 1 (2011-01-19), S.185-196 |
| Datensatznummer |
250012595
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| Publikation (Nr.) |
 copernicus.org/hess-15-185-2011.pdf |
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| Zusammenfassung |
| Pumping stations play an important role in flood mitigation in metropolitan
areas. The existing sewerage systems, however, are facing a great challenge
of fast rising peak flow resulting from urbanization and climate change. It
is imperative to construct an efficient and accurate operating prediction
model for pumping stations to simulate the drainage mechanism for
discharging the rainwater in advance. In this study, we propose two
rule-based fuzzy neural networks, adaptive neuro-fuzzy inference system
(ANFIS) and counterpropagation fuzzy neural network for on-line predicting
of the number of open and closed pumps of a pivotal pumping station in
Taipei city up to a lead time of 20 min. The performance of ANFIS
outperforms that of CFNN in terms of model efficiency, accuracy, and
correctness. Furthermore, the results not only show the predictive water
levels do contribute to the successfully operating pumping stations but also
demonstrate the applicability and reliability of ANFIS in automatically
controlling the urban sewerage systems. |
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