Identifying low impact development strategies for flood mitigation using a fuzzy-probabilistic approach

• Published in: Environmental modelling & software : with environment data news Vol. 60; pp. 31 - 44
• Main Authors: Yazdi, J., Salehi Neyshabouri, S.A.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2014; Elsevier
• Subjects: algorithms; Animal, plant and microbial ecology; ANN; Biological and medical sciences; case studies; computer software; Copula; cost effectiveness; environmental models; Flood risk; Floods; Fundamental and applied biological sciences. Psychology; Fuzzy; General aspects. Techniques; Learning theory; Low impact development; Mathematical analysis; Mathematical models; mathematical theory; Methodology; Methods and techniques (sampling, tagging, trapping, modelling...); Neural networks; NSGA-II; Optimization; risk management; runoff; Strategy; Uncertainty; Low impact development; NSGA-II; ANN; Uncertainty; Copula; Flood risk; Fuzzy; Optimization; Probabilistic approach; Risk; Flood; Development; Mitigation measure; Strategy
• ISSN: 1364-8152
• DOI: 10.1016/j.envsoft.2014.06.004

Low impact development (LID) includes strategies and practices that are designed to control surface runoff at its sources in a sustainable way. The performance of these strategies has been frequently addressed through curve number approach. This approach however subjects to a great deal of uncertainties owing to uncertain nature of curve numbers and temporal/spatial variability of flood events. This paper represents a novel methodology to deal with both inherent flood uncertainties and epistemic uncertainties identifying optimal LID strategies for flood mitigation. The proposed methodology integrates a great variety of mathematical tools including copula functions, MCS method, hydrological and hydraulic models, NSGA-II algorithm as well as ANN and fuzzy set theory. The obtained results from a case study clearly demonstrate that the proposed methodology not only presents cost-effective measures, but also can simultaneously handle both inherent and epistemic uncertainties in flood risk management. •We proposed a methodology to determine optimal land use strategies for flood mitigation.•The methodology captures inherent flood uncertainties and epistemic uncertainties.•Flood uncertainties are considered through statistical copula functions within MCS.•Epistemic uncertainties are considered through fuzzy set theory.•The algorithm integrates NSGA-II, hydrological and hydraulic models and ANN.