Robust multi-objective optimization under multiple-uncertainties using CM-ROPAR approach: case study of the water resources allocation in the Huaihe River Basin
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: open (until 23 Jun 2023)
- RC1: 'Comment on hess-2023-57', Anonymous Referee #1, 26 May 2023 reply
Jitao Zhang et al.
Jitao Zhang et al.
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thank you for the possibility of reviewing your interesting contribution. This paper proposes a procedure to optimally allocate water under uncertain conditions facing the problem of developing a robust procedure in order to support decision processes in a continuously changing scenario and data uncertainty. The contribution of this work is to present a new version of ROPAR including a probability of different scenarios based on copula functions, a robust strategy to analyse the Pareto frontier of two object functions, and a final robustness dependence analysis between robustness and the two object function for best strategy definition.
As such, the topic of the paper is well within the scope of the journal and the readership would benefit from the presented analysis. Optimal water management strategies are a current crucial task in a time of significant changes driven by climate change. Additionally, the need to exploit data and the related inaccuracy in optimisation, prediction and decision-making tools is an actual challenge involving the water sector, and more in general engineering applications.
The novelty proposed by the proposed work is clearly explained in the introduction and consists in the improvement of the ROPAR procedure by introducing multivariate uncertainty analysis by copulas. Nevertheless, I would suggest avoiding bulleted lists to introduce discretional topics such as in lines 75-80.
Please, avoid unnecessary paragraphs, such as line 88.
In general, avoid abusing acronyms, especially the lesser-used ones, as they impede fluent reading.
What is the function of a figure if it is not described in the text? Where is the flowchart proposed in Figure 1 described? The structure of the paper needs to be supported by the various section references.
It would be preferable to maintain a more consonant structure of the manuscript, introducing the case study after the methodology.
Line 120: “Copula functions are mainly classified into Archimedean, elliptic, and quadratic types.” I don't think this statement is true, who states this? There are other widely used copula classes.
Section 3.1 describes a general copula analysis without any reference about the proposed work and use of copulas in the analysis.
Line 150-153. The introduction of the uncertainty through a normal distribution with mean 1 and sd 0.05 is not clear. Why this distribution and these values?
The list proposed from lines 143 to 171 is not properly explained. Avoid the technical list without proper explanation, please include the text in paragraphs describing comprehensively the procedure.
The methodology needs to be rewritten and presented in a less confusing way and commented on more comprehensively.
Even if NSGA-II algorithm is a well-known optimizer, please provide more information about the setup of this algorithm, population size, generation, etc.
The main drawback of the proposed methodology is the lack of flexibility due to the severe limitation such as the number of uncertainty and the object function that can be included in the analysis, both equal to two. Flexibility and easily interpretability are crucial characteristics in the decision-making process, for this reason, I would like to know how the authors should overcome this limit and generalise the proposed methodology.
Finally, I suggest mentioning in the conclusion a summary of what comes out from the case study analysis. It could be a benefit for highlighting and quantifying the actual pros of the new proposed methodology.