TY - JOUR
T1 - Handling uncertainty in optimal design of reservoir water quality monitoring systems
AU - Pourshahabi, Shokoufeh
AU - Rakhshandehroo, Gholamreza
AU - Talebbeydokhti, Nasser
AU - Nikoo, Mohammad Reza
AU - Masoumi, Fariborz
N1 - Funding Information:
The authors would like to gratefully acknowledge Dr. Ye Tian and his colleagues for developing the PlatEMO tool that is a user-friendly publicly available platform for evolutionary multi-objective optimization. The authors would also like to thank Mr. Ehsan Raei, a graduate of Shiraz University, for writing a useful code in MATLAB? based on image processing for calculation of the IRWQI automatically.
Publisher Copyright:
© 2020 Elsevier Ltd
PY - 2020/11
Y1 - 2020/11
N2 - In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objective optimization problem. The proposed model is developed for the Karkheh reservoir system in Iran as a real-world problem. The results show excellent performance of the optimized water quality sampling locations instead of all potential ones in providing adequate information about the reservoir water quality status. The presented uncertainty-based model leads to a 55.73% reduction in the radius of the uncertain interval caused by different scenarios. Handling uncertainties in a spatio-temporal many-objective optimization problem is the main contribution of this study, yielding a reliable and robust design of a reservoir monitoring system that is less sensitive to various scenarios.
AB - In the present paper, a scenario-based many-objective optimization model is developed for the spatio-temporal optimal design of reservoir water quality monitoring systems considering uncertainties. The proposed methodology is based on the concept of nonlinear interval number programming and information theory, while handling uncertainties of temperature, reservoir inflow, and inflow constituent concentration. A reference-point-based non-dominated sorting genetic algorithm (NSGA-III) is used to deal with the many-objective optimization problem. The proposed model is developed for the Karkheh reservoir system in Iran as a real-world problem. The results show excellent performance of the optimized water quality sampling locations instead of all potential ones in providing adequate information about the reservoir water quality status. The presented uncertainty-based model leads to a 55.73% reduction in the radius of the uncertain interval caused by different scenarios. Handling uncertainties in a spatio-temporal many-objective optimization problem is the main contribution of this study, yielding a reliable and robust design of a reservoir monitoring system that is less sensitive to various scenarios.
KW - Information theory
KW - Many-objective optimization model
KW - Nonlinear interval number programming
KW - Reservoir monitoring system
KW - Uncertainty
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U2 - 10.1016/j.envpol.2020.115211
DO - 10.1016/j.envpol.2020.115211
M3 - Article
C2 - 32683163
AN - SCOPUS:85087894681
SN - 0269-7491
VL - 266
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 115211
ER -