Urban drainage decision model for storm emergency management based on multi-objective optimization

Abstract This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was...
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Autor*in:	Bao, Shitai [verfasserIn] Lai, Zehui Chen, Biao Chen, Shunqing

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Urban waterlogging Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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: Stochastic environmental research and risk assessment - Berlin : Springer, 1987, 37(2022), 3 vom: 18. Okt., Seite 813-829
Übergeordnetes Werk:	volume:37 ; year:2022 ; number:3 ; day:18 ; month:10 ; pages:813-829

Links:	Volltext

DOI / URN:	10.1007/s00477-022-02315-x

Katalog-ID:	SPR049534602

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520			\|a Abstract This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging.
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912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_250
912			\|a GBV_ILN_267
912			\|a GBV_ILN_281
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912			\|a GBV_ILN_293
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allfields	10.1007/s00477-022-02315-x doi (DE-627)SPR049534602 (SPR)s00477-022-02315-x-e DE-627 ger DE-627 rakwb eng Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://dx.doi.org/10.1007/s00477-022-02315-x 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_267 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_2119 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_4277 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 3 18 10 813-829
spelling	10.1007/s00477-022-02315-x doi (DE-627)SPR049534602 (SPR)s00477-022-02315-x-e DE-627 ger DE-627 rakwb eng Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://dx.doi.org/10.1007/s00477-022-02315-x 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_267 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_2119 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_4277 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 3 18 10 813-829
allfields_unstemmed	10.1007/s00477-022-02315-x doi (DE-627)SPR049534602 (SPR)s00477-022-02315-x-e DE-627 ger DE-627 rakwb eng Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://dx.doi.org/10.1007/s00477-022-02315-x 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_267 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_2119 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_4277 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 3 18 10 813-829
allfieldsGer	10.1007/s00477-022-02315-x doi (DE-627)SPR049534602 (SPR)s00477-022-02315-x-e DE-627 ger DE-627 rakwb eng Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://dx.doi.org/10.1007/s00477-022-02315-x 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_267 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_2119 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_4277 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 3 18 10 813-829
allfieldsSound	10.1007/s00477-022-02315-x doi (DE-627)SPR049534602 (SPR)s00477-022-02315-x-e DE-627 ger DE-627 rakwb eng Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213 Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Berlin : Springer, 1987 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)27160235X (DE-600)1481263-0 1436-3259 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://dx.doi.org/10.1007/s00477-022-02315-x 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_267 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_2119 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_4277 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 3 18 10 813-829
language	English
source	Enthalten in Stochastic environmental research and risk assessment 37(2022), 3 vom: 18. Okt., Seite 813-829 volume:37 year:2022 number:3 day:18 month:10 pages:813-829
sourceStr	Enthalten in Stochastic environmental research and risk assessment 37(2022), 3 vom: 18. Okt., Seite 813-829 volume:37 year:2022 number:3 day:18 month:10 pages:813-829
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Urban waterlogging Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization
isfreeaccess_bool	false
container_title	Stochastic environmental research and risk assessment
authorswithroles_txt_mv	Bao, Shitai @@aut@@ Lai, Zehui @@aut@@ Chen, Biao @@aut@@ Chen, Shunqing @@aut@@
publishDateDaySort_date	2022-10-18T00:00:00Z
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author	Bao, Shitai
spellingShingle	Bao, Shitai misc Urban waterlogging misc Surface water simulation misc Cellular automata misc Drainage optimization modeling misc Multi-objective optimization Urban drainage decision model for storm emergency management based on multi-objective optimization
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topic_title	Urban drainage decision model for storm emergency management based on multi-objective optimization Urban waterlogging (dpeaa)DE-He213 Surface water simulation (dpeaa)DE-He213 Cellular automata (dpeaa)DE-He213 Drainage optimization modeling (dpeaa)DE-He213 Multi-objective optimization (dpeaa)DE-He213
topic	misc Urban waterlogging misc Surface water simulation misc Cellular automata misc Drainage optimization modeling misc Multi-objective optimization
topic_unstemmed	misc Urban waterlogging misc Surface water simulation misc Cellular automata misc Drainage optimization modeling misc Multi-objective optimization
topic_browse	misc Urban waterlogging misc Surface water simulation misc Cellular automata misc Drainage optimization modeling misc Multi-objective optimization
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title	Urban drainage decision model for storm emergency management based on multi-objective optimization
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title_full	Urban drainage decision model for storm emergency management based on multi-objective optimization
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title_sort	urban drainage decision model for storm emergency management based on multi-objective optimization
title_auth	Urban drainage decision model for storm emergency management based on multi-objective optimization
abstract	Abstract This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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 This study proposes a solution to prevent urban waterlogging, a challenging environmental issue, by integrating waterlogging prediction and drainage optimization schemes based on cellular automaton and multi-objective optimization theories. An urban waterlogging model for uncertain flow was constructed considering urban surface fragmentation and space complexity. For dynamic simulation, outputs of water depth and flooded areas were projected with inputs of rainfall, soil infiltration, plant interception, gully discharge, and outflow to its neighbors in each cell at any moment. Using a multi-objective optimization approach, the drainage decision model for optimal solutions calculated the maximal amount of water for pumping from flooded zones to candidate reservoirs with minimal energy cost. This integrated approach was successfully applied to the DongHaoChong catchment, an 11 $ km^{2} $ watershed in Guangzhou, southern China and validated by comparing the simulated flood areas with flooded points from two historical rainstorms (August 15, 2013 and June 23, 2014). The RMSE of the maximum waterlogging depth were 26.89 and 78.48 mm, respectively. Therefore, the proposed model was reliable and capable of simulating uncertain flow at any position and moment with minimal data input and parameters in an urban environment. Water-logged area and depth could be predicted based on the given rainfall assuming 1-, 10-, 50-, and 100-year storm data in the study area, and optimal drainage solutions could be obtained and verified. The proposed urban waterlogging prediction and drainage emergency models could optimize decision-making to improve emergency plans and reduce losses due to urban waterlogging. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor 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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container_issue	3
title_short	Urban drainage decision model for storm emergency management based on multi-objective optimization
url	https://dx.doi.org/10.1007/s00477-022-02315-x
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author2	Lai, Zehui Chen, Biao Chen, Shunqing
author2Str	Lai, Zehui Chen, Biao Chen, Shunqing
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doi_str	10.1007/s00477-022-02315-x
up_date	2024-07-04T01:13:02.822Z
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Urban drainage decision model for storm emergency management based on multi-objective optimization

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