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

Abstract

Urban floods cause massive damage to infrastructure and loss of life. 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. Historical extreme rainfall event of 237.5 mm which occurred in year 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 employed 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 event 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.