Handling uncertainty in optimal design of reservoir water quality monitoring systems.

Autor: Pourshahabi S; Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran. Electronic address: pshahabi@shirazu.ac.ir., Rakhshandehroo G; Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran. Electronic address: rakhshan@shirazu.ac.ir., Talebbeydokhti N; Department of Civil and Environmental Engineering, Head of Environmental Research and Sustainable Development Center, Shiraz University, Shiraz, Iran. Electronic address: taleb@shirazu.ac.ir., Nikoo MR; Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran. Electronic address: nikoo@shirazu.ac.ir., Masoumi F; Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran. Electronic address: f_masoumi@uma.ac.ir.
Jazyk: angličtina
Zdroj: Environmental pollution (Barking, Essex : 1987) [Environ Pollut] 2020 Nov; Vol. 266 (Pt 2), pp. 115211. Date of Electronic Publication: 2020 Jul 10.
DOI: 10.1016/j.envpol.2020.115211
Abstrakt: 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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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