TY - JOUR
T1 - A Stochastic Conflict Resolution Optimization Model for Flood Management in Detention Basins
T2 - Application of Fuzzy Graph Model
AU - Nematollahi, Banafsheh
AU - Bakhtiari, Parnian Hashempour
AU - Talebbeydokhti, Nasser
AU - Rakhshandehroo, Gholam Reza
AU - Nikoo, Mohammad Reza
AU - Gandomi, Amir H.
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - Floods are a natural disaster of significant concern because of their considerable damages to people’s livelihood. To this extent, there is a critical need to enhance flood management techniques by establishing proper infrastructure, such as detention basins. Although intelligent models may be adopted for flood management by detention basins, there is a literature gap on the optimum design of such structures while facing flood risks. The presented study filled this research gap by introducing a methodology to obtain the optimum design of detention basins using a stochastic conflict resolution optimization model considering inflow hydrographs uncertainties. This optimization model was developed by minimizing the conditional value-at-risk (CvaR) of flood overtopping, downstream flood damage, and deficit risk of water demand, as well as the deviation of flood overtopping and downstream damage based on non-linear interval number programming (NINP), for four different outlets types via a robust optimization tool, namely the non-dominated sorting genetic algorithm-III (NSGA-III). Conflict resolution was performed using the graph model for conflict resolution (GMCR) technique, enhanced by fuzzy preferences, to comply with the authorities’ priorities. Results indicated that the proposed framework could effectively design optimum detention basins consistent with the regional and hydrological standards.
AB - Floods are a natural disaster of significant concern because of their considerable damages to people’s livelihood. To this extent, there is a critical need to enhance flood management techniques by establishing proper infrastructure, such as detention basins. Although intelligent models may be adopted for flood management by detention basins, there is a literature gap on the optimum design of such structures while facing flood risks. The presented study filled this research gap by introducing a methodology to obtain the optimum design of detention basins using a stochastic conflict resolution optimization model considering inflow hydrographs uncertainties. This optimization model was developed by minimizing the conditional value-at-risk (CvaR) of flood overtopping, downstream flood damage, and deficit risk of water demand, as well as the deviation of flood overtopping and downstream damage based on non-linear interval number programming (NINP), for four different outlets types via a robust optimization tool, namely the non-dominated sorting genetic algorithm-III (NSGA-III). Conflict resolution was performed using the graph model for conflict resolution (GMCR) technique, enhanced by fuzzy preferences, to comply with the authorities’ priorities. Results indicated that the proposed framework could effectively design optimum detention basins consistent with the regional and hydrological standards.
KW - Conditional value-at-risk (CVaR) method
KW - Flood management
KW - Fuzzy preferences
KW - Graph model for conflict resolution (GMCR)
KW - Non-dominated sorting genetic algorithm-III (NSGA-III)
KW - Non-linear interval number programming (NINP)
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U2 - 10.3390/w14050774
DO - 10.3390/w14050774
M3 - Article
AN - SCOPUS:85125763226
SN - 2073-4441
VL - 14
JO - Water (Switzerland)
JF - Water (Switzerland)
IS - 5
M1 - 774
ER -