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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/77240


    Title: Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks
    Authors: Chiang, Y.M.;Chang, L.C.;Tsai, M.J.;Wang, Y.F.;Chang, F.J.
    Contributors: 淡江大學水資源及環境工程學系
    Date: 2011
    Issue Date: 2012-06-14 09:11:30 (UTC+8)
    Publisher: Goettingen: Copernicus GmbH
    Abstract: 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.
    Relation: Hydrology and Earth System Sciences 15(1), pp.185-196
    DOI: 10.5194/hess-15-185-2011
    Appears in Collections:[水資源及環境工程學系暨研究所] 期刊論文

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