04 Apr 2023
 | 04 Apr 2023
Status: this preprint is currently under review for the journal HESS.

Robust multi-objective optimization under multiple-uncertainties using CM-ROPAR approach: case study of the water resources allocation in the Huaihe River Basin

Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong

Abstract. Water resources managers need to make decisions in a constantly changing environment because the data relating to water resources is uncertain and imprecise. The Robust Optimization and Probabilistic Analysis of Robustness (ROPAR) algorithm is a well-suited tool for dealing with uncertainty. Still, the failure to consider multiple uncertainties and multi-objective robustness hinder the application of the ROPAR algorithm to practical problems. This paper proposes a robust optimization and robustness probabilistic analysis method that considers numerous uncertainties and multi-objective robustness for robust water resources allocation under uncertainty. The Copula function is introduced for analyzing the probabilities of different scenarios. The robustness with respect to the two objective functions is analyzed separately, and the Pareto frontier of robustness is generated. The relationship between the robustness with respect to the two objective functions is used to evaluate water resources management strategies. Use of the method is illustrated on a case study of water resources allocation in the Huaihe River Basin. The results demonstrate that the method opens a possibility for water managers to make more informed uncertainty-aware decisions.

Jitao Zhang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2023-57', Anonymous Referee #1, 26 May 2023
  • RC2: 'Comment on hess-2023-57', Anonymous Referee #2, 20 Nov 2023

Jitao Zhang et al.

Jitao Zhang et al.


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Short summary
This paper proposes a coupled multi-objective robust optimisation and probabilistic analysis of robustness algorithm. The algorithm is applied to the Huaihe River basin in China. It is shown that the robustness of the solution obtained by the algorithm is significantly better than that of the traditional deterministic model solution, demonstrating the usefulness of the algorithm. In addition to this, robustness between multiple objectives is presented for the first time in this paper.