Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario

This research is being carried out to study how best management practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of...

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Autores principales:	Rohit Dwivedula, R. Madhuri, K. Srinivasa Raju, A. Vasan
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bmps c-taea multiobjective optimisation nsga-iii rcp 2.6 urban floods Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/dbb85e8a5de6411c9e0cbb70c60228a9
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spelling	oai:doaj.org-article:dbb85e8a5de6411c9e0cbb70c60228a92021-11-06T11:18:30ZMultiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario0273-12231996-973210.2166/wst.2021.283https://doaj.org/article/dbb85e8a5de6411c9e0cbb70c60228a92021-08-01T00:00:00Zhttp://wst.iwaponline.com/content/84/4/966https://doaj.org/toc/0273-1223https://doaj.org/toc/1996-9732This research is being carried out to study how best management practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of pollutants from surface runoff and sustainable utilisation of land. This situation is framed as a multiobjective optimisation problem to analyse the trade-offs between multiple goals of runoff reduction, construction cost and pollutant load reduction. Output includes a wide range of choices to choose from for decision makers. Proposed methodology is demonstrated with a case study of Greater Hyderabad Municipal Corporation (GHMC), India. A historical extreme rainfall event of 237.5 mm which occurred in 2016 and extreme rainfall event of 1,740.62 mm corresponding to representative concentration pathway (RCP) 2.6 were considered for analysis. Two multiobjective optimisation algorithms, namely non-dominated sorting genetic algorithm-III (NSGA-III) and constrained two-archive evolutionary algorithm (C-TAEA) are used to solve the BMP placement problem, following which the resulting Pareto-fronts are ensembled. K-Medoids-based cluster analysis is performed on the resulting ensembled Pareto-front. The proposed ensembled approach identified ten possible BMP configurations, with costs ranging from Rs. to surface runoff reduction ranging from to and pollutant load removal ranging from tonnes. Use of BMPs in future events has the potential to reduce surface runoff from , while simultaneously removing tonnes of pollutants for cost ranging from The proposed framework forms an effective and novel way to characterise and solve BMP optimisation problems in context of climate change, presenting a view of the urban flooding scenario today, and the likely course of events in the future. HIGHLIGHTS The multiobjective BMP problem is solved using NSGA-III and C-TAEA.; Cluster analysis is performed using K-Medoids.; Accounting for climate change through RCP 2.6.;Rohit DwivedulaR. MadhuriK. Srinivasa RajuA. VasanIWA Publishingarticlebmpsc-taeamultiobjective optimisationnsga-iiircp 2.6urban floodsEnvironmental technology. Sanitary engineeringTD1-1066ENWater Science and Technology, Vol 84, Iss 4, Pp 966-984 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bmps c-taea multiobjective optimisation nsga-iii rcp 2.6 urban floods Environmental technology. Sanitary engineering TD1-1066
spellingShingle	bmps c-taea multiobjective optimisation nsga-iii rcp 2.6 urban floods Environmental technology. Sanitary engineering TD1-1066 Rohit Dwivedula R. Madhuri K. Srinivasa Raju A. Vasan Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
description	This research is being carried out to study how best management practices (BMPs) can mitigate the negative effects of urban floods during extreme rainfall events. Strategically placing BMPs throughout open areas and rooftops in urban areas serves multiple purposes of storage of rainwater, removal of pollutants from surface runoff and sustainable utilisation of land. This situation is framed as a multiobjective optimisation problem to analyse the trade-offs between multiple goals of runoff reduction, construction cost and pollutant load reduction. Output includes a wide range of choices to choose from for decision makers. Proposed methodology is demonstrated with a case study of Greater Hyderabad Municipal Corporation (GHMC), India. A historical extreme rainfall event of 237.5 mm which occurred in 2016 and extreme rainfall event of 1,740.62 mm corresponding to representative concentration pathway (RCP) 2.6 were considered for analysis. Two multiobjective optimisation algorithms, namely non-dominated sorting genetic algorithm-III (NSGA-III) and constrained two-archive evolutionary algorithm (C-TAEA) are used to solve the BMP placement problem, following which the resulting Pareto-fronts are ensembled. K-Medoids-based cluster analysis is performed on the resulting ensembled Pareto-front. The proposed ensembled approach identified ten possible BMP configurations, with costs ranging from Rs. to surface runoff reduction ranging from to and pollutant load removal ranging from tonnes. Use of BMPs in future events has the potential to reduce surface runoff from , while simultaneously removing tonnes of pollutants for cost ranging from The proposed framework forms an effective and novel way to characterise and solve BMP optimisation problems in context of climate change, presenting a view of the urban flooding scenario today, and the likely course of events in the future. HIGHLIGHTS The multiobjective BMP problem is solved using NSGA-III and C-TAEA.; Cluster analysis is performed using K-Medoids.; Accounting for climate change through RCP 2.6.;
format	article
author	Rohit Dwivedula R. Madhuri K. Srinivasa Raju A. Vasan
author_facet	Rohit Dwivedula R. Madhuri K. Srinivasa Raju A. Vasan
author_sort	Rohit Dwivedula
title	Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
title_short	Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
title_full	Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
title_fullStr	Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
title_full_unstemmed	Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
title_sort	multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/dbb85e8a5de6411c9e0cbb70c60228a9
work_keys_str_mv	AT rohitdwivedula multiobjectiveoptimisationandclusteranalysisinplacementofbestmanagementpracticesinanurbanfloodingscenario AT rmadhuri multiobjectiveoptimisationandclusteranalysisinplacementofbestmanagementpracticesinanurbanfloodingscenario AT ksrinivasaraju multiobjectiveoptimisationandclusteranalysisinplacementofbestmanagementpracticesinanurbanfloodingscenario AT avasan multiobjectiveoptimisationandclusteranalysisinplacementofbestmanagementpracticesinanurbanfloodingscenario
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Multiobjective optimisation and cluster analysis in placement of best management practices in an urban flooding scenario

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