Handling uncertainty in optimal design of reservoir water quality monitoring systems

Shokoufeh Pourshahabi, Gholamreza Rakhshandehroo, Nasser Talebbeydokhti, Mohammad Reza Nikoo*, Fariborz Masoumi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


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.

Original languageEnglish
Article number115211
JournalEnvironmental Pollution
Publication statusPublished - Nov 2020


  • Information theory
  • Many-objective optimization model
  • Nonlinear interval number programming
  • Reservoir monitoring system
  • Uncertainty

ASJC Scopus subject areas

  • Toxicology
  • Pollution
  • Health, Toxicology and Mutagenesis


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