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

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 uncertain...

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Vydané v:Environmental modelling & software : with environment data news Ročník 60; s. 31 - 44
Hlavní autori: Yazdi, J., Salehi Neyshabouri, S.A.A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Elsevier Ltd 01.10.2014
Elsevier
Predmet:
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
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Popis
Shrnutí: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.
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ISSN:1364-8152
DOI:10.1016/j.envsoft.2014.06.004