Development of the SWMM Hydraulic Parameters Optimization Model

碩士 === 中原大學 === 土木工程研究所 === 99 === This research develops an SWMM Parameters Optimization Model (SPOM) to reduce the cost of time in calibrating parameters of SWMM automatically. It uses Genetic Algorithm Library for optimizing the hydrological and geographical parameters of SWMM-RUNOFF module and S...

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Main Authors: Shang-Fu Lu, 盧尚甫
Other Authors: Shiu-Shin Lin
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/90694188480011605428
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spelling ndltd-TW-099CYCU50150432015-10-13T20:23:26Z http://ndltd.ncl.edu.tw/handle/90694188480011605428 Development of the SWMM Hydraulic Parameters Optimization Model SWMM水理參數優選模式之開發 Shang-Fu Lu 盧尚甫 碩士 中原大學 土木工程研究所 99 This research develops an SWMM Parameters Optimization Model (SPOM) to reduce the cost of time in calibrating parameters of SWMM automatically. It uses Genetic Algorithm Library for optimizing the hydrological and geographical parameters of SWMM-RUNOFF module and SWMM-EXTRAN module. The SPOM is used as the core of WEB SWMM Parameters Optimization Model (WEB-SPOM) to facilitate the operations of SPOM. In this research, Zhung-Gung drainage system in Taipei city is employed as a case study, and the past typhoon rainfall and actual manhole water level are collected to be the input data of SPOM. First, SPOM is used to verify the optimum parameter from nine typhoon events one by one, and confer the accuracy of interior Genetic Algorithm parameters. Then, the accuracy of parameters from the nine typhoon events is validated. Finally, the optimum parameter differences between simulated manhole water level and actual manhole water level are compared; the parameters and efficiency before and after optimization are analyzed. USWS (urban sewer warning system), a web-based system, is improved by replacing the optimized parameters of SWMM and imposing SPOM on USWS to demonstrate the proposed approach. Shiu-Shin Lin 林旭信 2011 學位論文 ; thesis 84 zh-TW
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language zh-TW
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description 碩士 === 中原大學 === 土木工程研究所 === 99 === This research develops an SWMM Parameters Optimization Model (SPOM) to reduce the cost of time in calibrating parameters of SWMM automatically. It uses Genetic Algorithm Library for optimizing the hydrological and geographical parameters of SWMM-RUNOFF module and SWMM-EXTRAN module. The SPOM is used as the core of WEB SWMM Parameters Optimization Model (WEB-SPOM) to facilitate the operations of SPOM. In this research, Zhung-Gung drainage system in Taipei city is employed as a case study, and the past typhoon rainfall and actual manhole water level are collected to be the input data of SPOM. First, SPOM is used to verify the optimum parameter from nine typhoon events one by one, and confer the accuracy of interior Genetic Algorithm parameters. Then, the accuracy of parameters from the nine typhoon events is validated. Finally, the optimum parameter differences between simulated manhole water level and actual manhole water level are compared; the parameters and efficiency before and after optimization are analyzed. USWS (urban sewer warning system), a web-based system, is improved by replacing the optimized parameters of SWMM and imposing SPOM on USWS to demonstrate the proposed approach.
author2 Shiu-Shin Lin
author_facet Shiu-Shin Lin
Shang-Fu Lu
盧尚甫
author Shang-Fu Lu
盧尚甫
spellingShingle Shang-Fu Lu
盧尚甫
Development of the SWMM Hydraulic Parameters Optimization Model
author_sort Shang-Fu Lu
title Development of the SWMM Hydraulic Parameters Optimization Model
title_short Development of the SWMM Hydraulic Parameters Optimization Model
title_full Development of the SWMM Hydraulic Parameters Optimization Model
title_fullStr Development of the SWMM Hydraulic Parameters Optimization Model
title_full_unstemmed Development of the SWMM Hydraulic Parameters Optimization Model
title_sort development of the swmm hydraulic parameters optimization model
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/90694188480011605428
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AT shangfulu swmmshuǐlǐcānshùyōuxuǎnmóshìzhīkāifā
AT lúshàngfǔ swmmshuǐlǐcānshùyōuxuǎnmóshìzhīkāifā
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