Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability

Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couple...
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Autor*in:	Xie, Kun [verfasserIn] Kim, Jong-Suk Hu, Linjuan Chen, Hua Xu, Chong-Yu Lee, Jung Hwan Chen, Jie Yoon, Sun-Kwon Zhu, Di Zhang, Shaobo Liu, Yang

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Urban drainage system Urban flooding Simulation optimization Adaptation strategy Mitigation strategy

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Water resources management - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987, 37(2022), 1 vom: 20. Okt., Seite 91-111
Übergeordnetes Werk:	volume:37 ; year:2022 ; number:1 ; day:20 ; month:10 ; pages:91-111

Links:	Volltext

DOI / URN:	10.1007/s11269-022-03357-0

Katalog-ID:	SPR04903300X

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520			\|a Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding.
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allfields	10.1007/s11269-022-03357-0 doi (DE-627)SPR04903300X (SPR)s11269-022-03357-0-e DE-627 ger DE-627 rakwb eng Xie, Kun verfasserin aut Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213 Kim, Jong-Suk aut Hu, Linjuan aut Chen, Hua (orcid)0000-0002-2320-3228 aut Xu, Chong-Yu aut Lee, Jung Hwan aut Chen, Jie aut Yoon, Sun-Kwon aut Zhu, Di aut Zhang, Shaobo aut Liu, Yang aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 37(2022), 1 vom: 20. Okt., Seite 91-111 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:37 year:2022 number:1 day:20 month:10 pages:91-111 https://dx.doi.org/10.1007/s11269-022-03357-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 1 20 10 91-111
spelling	10.1007/s11269-022-03357-0 doi (DE-627)SPR04903300X (SPR)s11269-022-03357-0-e DE-627 ger DE-627 rakwb eng Xie, Kun verfasserin aut Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213 Kim, Jong-Suk aut Hu, Linjuan aut Chen, Hua (orcid)0000-0002-2320-3228 aut Xu, Chong-Yu aut Lee, Jung Hwan aut Chen, Jie aut Yoon, Sun-Kwon aut Zhu, Di aut Zhang, Shaobo aut Liu, Yang aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 37(2022), 1 vom: 20. Okt., Seite 91-111 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:37 year:2022 number:1 day:20 month:10 pages:91-111 https://dx.doi.org/10.1007/s11269-022-03357-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 1 20 10 91-111
allfields_unstemmed	10.1007/s11269-022-03357-0 doi (DE-627)SPR04903300X (SPR)s11269-022-03357-0-e DE-627 ger DE-627 rakwb eng Xie, Kun verfasserin aut Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213 Kim, Jong-Suk aut Hu, Linjuan aut Chen, Hua (orcid)0000-0002-2320-3228 aut Xu, Chong-Yu aut Lee, Jung Hwan aut Chen, Jie aut Yoon, Sun-Kwon aut Zhu, Di aut Zhang, Shaobo aut Liu, Yang aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 37(2022), 1 vom: 20. Okt., Seite 91-111 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:37 year:2022 number:1 day:20 month:10 pages:91-111 https://dx.doi.org/10.1007/s11269-022-03357-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 1 20 10 91-111
allfieldsGer	10.1007/s11269-022-03357-0 doi (DE-627)SPR04903300X (SPR)s11269-022-03357-0-e DE-627 ger DE-627 rakwb eng Xie, Kun verfasserin aut Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213 Kim, Jong-Suk aut Hu, Linjuan aut Chen, Hua (orcid)0000-0002-2320-3228 aut Xu, Chong-Yu aut Lee, Jung Hwan aut Chen, Jie aut Yoon, Sun-Kwon aut Zhu, Di aut Zhang, Shaobo aut Liu, Yang aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 37(2022), 1 vom: 20. Okt., Seite 91-111 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:37 year:2022 number:1 day:20 month:10 pages:91-111 https://dx.doi.org/10.1007/s11269-022-03357-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 1 20 10 91-111
allfieldsSound	10.1007/s11269-022-03357-0 doi (DE-627)SPR04903300X (SPR)s11269-022-03357-0-e DE-627 ger DE-627 rakwb eng Xie, Kun verfasserin aut Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213 Kim, Jong-Suk aut Hu, Linjuan aut Chen, Hua (orcid)0000-0002-2320-3228 aut Xu, Chong-Yu aut Lee, Jung Hwan aut Chen, Jie aut Yoon, Sun-Kwon aut Zhu, Di aut Zhang, Shaobo aut Liu, Yang aut Enthalten in Water resources management Dordrecht [u.a.] : Springer Science + Business Media B.V, 1987 37(2022), 1 vom: 20. Okt., Seite 91-111 (DE-627)315299924 (DE-600)2016360-5 1573-1650 nnns volume:37 year:2022 number:1 day:20 month:10 pages:91-111 https://dx.doi.org/10.1007/s11269-022-03357-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 37 2022 1 20 10 91-111
language	English
source	Enthalten in Water resources management 37(2022), 1 vom: 20. Okt., Seite 91-111 volume:37 year:2022 number:1 day:20 month:10 pages:91-111
sourceStr	Enthalten in Water resources management 37(2022), 1 vom: 20. Okt., Seite 91-111 volume:37 year:2022 number:1 day:20 month:10 pages:91-111
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Urban drainage system Urban flooding Simulation optimization Adaptation strategy Mitigation strategy
isfreeaccess_bool	false
container_title	Water resources management
authorswithroles_txt_mv	Xie, Kun @@aut@@ Kim, Jong-Suk @@aut@@ Hu, Linjuan @@aut@@ Chen, Hua @@aut@@ Xu, Chong-Yu @@aut@@ Lee, Jung Hwan @@aut@@ Chen, Jie @@aut@@ Yoon, Sun-Kwon @@aut@@ Zhu, Di @@aut@@ Zhang, Shaobo @@aut@@ Liu, Yang @@aut@@
publishDateDaySort_date	2022-10-20T00:00:00Z
hierarchy_top_id	315299924
id	SPR04903300X
language_de	englisch
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author	Xie, Kun
spellingShingle	Xie, Kun misc Urban drainage system misc Urban flooding misc Simulation optimization misc Adaptation strategy misc Mitigation strategy Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability
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topic_title	Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability Urban drainage system (dpeaa)DE-He213 Urban flooding (dpeaa)DE-He213 Simulation optimization (dpeaa)DE-He213 Adaptation strategy (dpeaa)DE-He213 Mitigation strategy (dpeaa)DE-He213
topic	misc Urban drainage system misc Urban flooding misc Simulation optimization misc Adaptation strategy misc Mitigation strategy
topic_unstemmed	misc Urban drainage system misc Urban flooding misc Simulation optimization misc Adaptation strategy misc Mitigation strategy
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title	Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability
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title_full	Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability
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author_browse	Xie, Kun Kim, Jong-Suk Hu, Linjuan Chen, Hua Xu, Chong-Yu Lee, Jung Hwan Chen, Jie Yoon, Sun-Kwon Zhu, Di Zhang, Shaobo Liu, Yang
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title_sort	intelligent scheduling of urban drainage systems: effective local adaptation strategies for increased climate variability
title_auth	Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability
abstract	Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Intelligent scheduling of urban drainage systems is generally regarded as a potentially sustainable strategy for urban flood management. To investigate the effectiveness of the intelligent scheduling strategy in mitigating urban flooding, a new intelligent scheduling model (ISM) that couples the Storm Water Management Model (SWMM) and a multiobjective particle swarm optimization algorithm is proposed for a simulation–optimization framework. The objectives of the ISM are to minimize the flooding volume, front-pool water level fluctuation, and operational cost. Synthetic rainfall events with different durations and return periods based on the Gumbel distribution and observed rainfall events are utilized to comprehensively assess the designed model's performance in the Dealim3 catchment, South Korea. The selected ISM-based scheduling strategies are assessed in accordance with climate change mitigation (i.e., reducing greenhouse gas emissions) and local adaptation strategies (i.e., improving drainage systems). The results indicate that these strategies generated by ISM lead to reductions in flooding, water level fluctuation, and operational costs. The maximum daily rainfall with a 100-year return period increased by 2.1% and 6.8% during 2025–2064 under SSP1-2.6 and SSP5-8.5, respectively, compared with the historical period (1975–2014), thereby increasing the magnitude of urban flooding. The ISM may also significantly lower the flooding process at specific nodes. The ISM-based strategy outperforms climate change mitigation and other adaptation strategies. This study shows that the ISM-based strategy are very useful to deal with the impact of climate change on urban flooding. © The Author(s), under exclusive licence to Springer Nature B.V. 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Intelligent Scheduling of Urban Drainage Systems: Effective Local Adaptation Strategies for Increased Climate Variability
url	https://dx.doi.org/10.1007/s11269-022-03357-0
remote_bool	true
author2	Kim, Jong-Suk Hu, Linjuan Chen, Hua Xu, Chong-Yu Lee, Jung Hwan Chen, Jie Yoon, Sun-Kwon Zhu, Di Zhang, Shaobo Liu, Yang
author2Str	Kim, Jong-Suk Hu, Linjuan Chen, Hua Xu, Chong-Yu Lee, Jung Hwan Chen, Jie Yoon, Sun-Kwon Zhu, Di Zhang, Shaobo Liu, Yang
ppnlink	315299924
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doi_str	10.1007/s11269-022-03357-0
up_date	2024-07-03T22:56:22.929Z
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