| Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks | |
|---|---|
| 學年 | 99 |
| 學期 | 1 |
| 出版（發表）日期 | 2011-01-01 |
| 作品名稱 | Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks |
| 作品名稱（其他語言） | |
| 著者 | Chiang, Y.M.; Chang, L.C.; Tsai, M.J.; Wang, Y.F.; Chang, F.J. |
| 單位 | 淡江大學水資源及環境工程學系 |
| 出版者 | Goettingen: Copernicus GmbH |
| 著錄名稱、卷期、頁數 | Hydrology and Earth System Sciences 15(1), pp.185-196 |
| 摘要 | 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. |
| 關鍵字 | |
| 語言 | en |
| ISSN | 1027-5606 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | |
| 國別 | DEU |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/77240 ) |
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