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  1. National Taiwan Ocean University Research Hub
  2. 海洋科學與資源學院
  3. 海洋環境資訊系
請用此 Handle URI 來引用此文件: http://scholars.ntou.edu.tw/handle/123456789/19578
DC 欄位值語言
dc.contributor.author黃建霖en_US
dc.contributor.author徐年盛en_US
dc.contributor.author魏志強en_US
dc.date.accessioned2022-01-03T08:04:50Z-
dc.date.available2022-01-03T08:04:50Z-
dc.date.issued2012-09-01-
dc.identifier.urihttp://scholars.ntou.edu.tw/handle/123456789/19578-
dc.description.abstract本研究目的為利用人工智慧技術研發都市排水抽水站即時操作之決策模式,以決定颱風與暴雨事件中排水閘門關閉時期抽水站抽水機組之即時操作方式。本研究研發ANFIS-HR及ANFIS-OPT兩種操作模式,ANFIS-HR操作模式為利用歷史操作紀錄結合調適性網路模糊推論系統(ANFIS)來建構模式:ANFIS-OPT操作模式則為利用禁忌、演算法所優選出歷史事件之最佳操作情況結合ANFIS來建構模式。本研究以新北市中港大排為研究區域,並以操作中前池最高水位以及最終水位與標的水位相差之絕對值來比較兩種操作模式與歷史操作之優劣,結果顯示ANFIS-OPT操作模式較ANFIS-HR操作模式及歷史操作紀錄為佳。The purpose of this study is to apply artificial intelligence techniques to propose the operation decision models, which can decide the real-time operational way of pumping machines in flood events during drainage gate closed period of a pumping station for an urban drainage system. This study develops two operation models, namely ANFIS-HR and ANFIS-OPT. ANFIS-HR is constructed by ANFIS (Adaptive Network-based Fuzzy Inference System) with historical operation records. ANFIS-OPT are constructed by ANFIS with the operation circumstances which are optimized by tabu search in historical events. This study uses Chung-Kong-Da-Pai basin as study area. The comparison variables which are simulated from real case by two operation models are maximum water level (WL) and absolute difference between final WL and target WL of pumping front-pool. Results show that ANFIS-OPT are better than ANFIS-HR and historical operation records.en_US
dc.language.isozhen_US
dc.relation.ispartof農業工程學報en_US
dc.subject抽水站操作en_US
dc.subject優選en_US
dc.subject調適性網路模糊推論系統en_US
dc.subject禁忌演算法en_US
dc.subjectPumping station operationen_US
dc.subjectOptimizationen_US
dc.subjectAdaptive Network-based Fuzzy Inference Systemen_US
dc.subjectTabu searchen_US
dc.title智慧型都市排水抽水站即時操作系統之研發en_US
dc.title.alternativeReal-Time Pumping Station Operation for an Urban Drainage System Using Artificial Intelligenceen_US
dc.typejournal articleen_US
dc.identifier.doi10.29974/JTAE.201209.0005-
dc.relation.journalvolume58en_US
dc.relation.journalissue3en_US
dc.relation.pages64 - 79en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.languageiso639-1zh-
item.fulltextno fulltext-
item.grantfulltextnone-
item.openairetypejournal article-
crisitem.author.deptCollege of Ocean Science and Resource-
crisitem.author.deptDepartment of Marine Environmental Informatics-
crisitem.author.deptNational Taiwan Ocean University,NTOU-
crisitem.author.deptCenter of Excellence for Ocean Engineering-
crisitem.author.deptData Analysis and Administrative Support-
crisitem.author.orcid0000-0002-2965-7538-
crisitem.author.parentorgNational Taiwan Ocean University,NTOU-
crisitem.author.parentorgCollege of Ocean Science and Resource-
crisitem.author.parentorgNational Taiwan Ocean University,NTOU-
crisitem.author.parentorgCenter of Excellence for Ocean Engineering-
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