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:	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 - Springer Berlin Heidelberg, 1999, 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:	OLC2134275529

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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.
650		4	\|a Urban waterlogging
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bklnumber	43.03$jMethoden der Umweltforschung und des Umweltschutzes 38.85$jHydrologie: Allgemeines 58.50$jUmwelttechnik: Allgemeines 52.23$jFluidtechnik
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allfields	10.1007/s00477-022-02315-x doi (DE-627)OLC2134275529 (DE-He213)s00477-022-02315-x-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Springer Berlin Heidelberg, 1999 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://doi.org/10.1007/s00477-022-02315-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 37 2022 3 18 10 813-829
spelling	10.1007/s00477-022-02315-x doi (DE-627)OLC2134275529 (DE-He213)s00477-022-02315-x-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Springer Berlin Heidelberg, 1999 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://doi.org/10.1007/s00477-022-02315-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 37 2022 3 18 10 813-829
allfields_unstemmed	10.1007/s00477-022-02315-x doi (DE-627)OLC2134275529 (DE-He213)s00477-022-02315-x-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Springer Berlin Heidelberg, 1999 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://doi.org/10.1007/s00477-022-02315-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 37 2022 3 18 10 813-829
allfieldsGer	10.1007/s00477-022-02315-x doi (DE-627)OLC2134275529 (DE-He213)s00477-022-02315-x-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Springer Berlin Heidelberg, 1999 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://doi.org/10.1007/s00477-022-02315-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 AR 37 2022 3 18 10 813-829
allfieldsSound	10.1007/s00477-022-02315-x doi (DE-627)OLC2134275529 (DE-He213)s00477-022-02315-x-p DE-627 ger DE-627 rakwb eng 333.7 VZ 550 VZ 43.03$jMethoden der Umweltforschung und des Umweltschutzes bkl 38.85$jHydrologie: Allgemeines bkl 58.50$jUmwelttechnik: Allgemeines bkl 52.23$jFluidtechnik bkl Bao, Shitai verfasserin aut Urban drainage decision model for storm emergency management based on multi-objective optimization 2022 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc 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 Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization Lai, Zehui aut Chen, Biao aut Chen, Shunqing (orcid)0000-0001-7794-0840 aut Enthalten in Stochastic environmental research and risk assessment Springer Berlin Heidelberg, 1999 37(2022), 3 vom: 18. Okt., Seite 813-829 (DE-627)269538283 (DE-600)1475430-7 (DE-576)077885473 1436-3240 nnns volume:37 year:2022 number:3 day:18 month:10 pages:813-829 https://doi.org/10.1007/s00477-022-02315-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 43.03$jMethoden der Umweltforschung und des Umweltschutzes VZ 106416952 (DE-625)106416952 38.85$jHydrologie: Allgemeines VZ 106421905 (DE-625)106421905 58.50$jUmwelttechnik: Allgemeines VZ 10641707X (DE-625)10641707X 52.23$jFluidtechnik VZ 106419870 (DE-625)106419870 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
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institution	findex.gbv.de
topic_facet	Urban waterlogging Surface water simulation Cellular automata Drainage optimization modeling Multi-objective optimization
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authorswithroles_txt_mv	Bao, Shitai @@aut@@ Lai, Zehui @@aut@@ Chen, Biao @@aut@@ Chen, Shunqing @@aut@@
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title	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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title_short	Urban drainage decision model for storm emergency management based on multi-objective optimization
url	https://doi.org/10.1007/s00477-022-02315-x
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author2	Lai, Zehui Chen, Biao Chen, Shunqing
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up_date	2024-07-04T00:21:01.865Z
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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. 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Okt., Seite 813-829</subfield><subfield code="w">(DE-627)269538283</subfield><subfield code="w">(DE-600)1475430-7</subfield><subfield code="w">(DE-576)077885473</subfield><subfield code="x">1436-3240</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:37</subfield><subfield code="g">year:2022</subfield><subfield code="g">number:3</subfield><subfield code="g">day:18</subfield><subfield code="g">month:10</subfield><subfield code="g">pages:813-829</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00477-022-02315-x</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-GEO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_267</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">43.03$jMethoden der Umweltforschung und des Umweltschutzes</subfield><subfield code="q">VZ</subfield><subfield code="0">106416952</subfield><subfield code="0">(DE-625)106416952</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">38.85$jHydrologie: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">106421905</subfield><subfield code="0">(DE-625)106421905</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.50$jUmwelttechnik: Allgemeines</subfield><subfield code="q">VZ</subfield><subfield code="0">10641707X</subfield><subfield code="0">(DE-625)10641707X</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">52.23$jFluidtechnik</subfield><subfield code="q">VZ</subfield><subfield code="0">106419870</subfield><subfield code="0">(DE-625)106419870</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">37</subfield><subfield code="j">2022</subfield><subfield code="e">3</subfield><subfield code="b">18</subfield><subfield code="c">10</subfield><subfield code="h">813-829</subfield></datafield></record></collection>
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Urban drainage decision model for storm emergency management based on multi-objective optimization

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