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Please use this identifier to cite or link to this item: http://scholars.ntou.edu.tw/handle/123456789/10895
DC FieldValueLanguage
dc.contributor.authorNien-Sheng Hsuen_US
dc.contributor.authorChien-Lin Huangen_US
dc.contributor.authorChih-Chiang Weien_US
dc.date.accessioned2020-11-21T06:54:18Z-
dc.date.available2020-11-21T06:54:18Z-
dc.date.issued2015-03-
dc.identifier.issn0022-1694-
dc.identifier.urihttp://scholars.ntou.edu.tw/handle/123456789/10895-
dc.description.abstractThis study applies an Adaptive Network-based Fuzzy Inference System (ANFIS) and a Real-Time Recurrent Learning Neural Network (RTRLNN) with an optimized reservoir release hydrograph using Mixed Integer Linear Programming (MILP) from historical typhoon events to develop a multi-phase intelligent real-time reservoir operation model for flood control. The flood control process is divided into three stages: (1) before flood (Stage I); (2) before peak flow (Stage II); and (3) after peak flow (Stage III). The models are then constructed with either three phase modules (ANFIS-3P and RTRLNN-3P) or two phase (Stage I + II and Stage III) modules (ANFIS-2P and RTRLNN-2P). The multi-phase modules are developed with consideration of the difference in operational decision mechanisms, decision information, release functions, and targets between each flood control stage to solve the problem of time-consuming computation and difficult system integration of MILP. In addition, the model inputs include the coupled short lead time and total reservoir inflow forecast information that are developed using radar- and satellite-based meteorological monitoring techniques, forecasted typhoon tracks, meteorological image similarity analysis, ANFIS and RTRLNN. This study uses the Tseng-Wen Reservoir basin as the study area, and the model results showed that RTRLNN outperformed ANFIS in the simulated outcomes from the optimized hydrographs. This study also applies the models to Typhoons Kalmaegi and Morakot to compare the simulations to historical operations. From the operation results, the RTRLNN-3P model is better than RTRLNN-2P and historical operations. Further, because the RTRLNN-3P model combines the innovative multi-phase module with monitored and forecasted decision information, the operation can simultaneously, effectively and automatically achieve the dual goals of flood detention at peak flow periods and water supply at the end of a typhoon event.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Hydrologyen_US
dc.subjectReservoir real-time flood operationen_US
dc.subjectOptimizationen_US
dc.subjectMulti-phase flood control moduleen_US
dc.subjectAdaptive Network-based Fuzzy Inference Systemen_US
dc.subjectReal-Time Recurrent Learning Neural Networken_US
dc.subjectReservoir inflow forecasten_US
dc.titleMulti-phase intelligent decision model for reservoir real-time flood control during typhoonsen_US
dc.typejournal articleen_US
dc.identifier.doi<Go to ISI>://WOS:000350920200002-
dc.identifier.doi<Go to ISI>://WOS:000350920200002-
dc.identifier.doi<Go to ISI>://WOS:000350920200002-
dc.identifier.doi10.1016/j.jhydrol.2014.12.013-
dc.identifier.doi<Go to ISI>://WOS:000350920200002-
dc.identifier.doi<Go to ISI>://WOS:000350920200002-
dc.identifier.url<Go to ISI>://WOS:000350920200002
dc.relation.journalvolume522en_US
dc.relation.journalissue10en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.languageiso639-1en-
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-
Appears in Collections:海洋環境資訊系
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