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.

本文针对考虑不确定性的储层水质监测系统时空优化设计，提出了一种基于场景的多目标优化模型。所提出的方法基于非线性区间数编程和信息论的概念，同时处理温度，储层流入量和流入成分浓度的不确定性。基于参考点的非支配排序遗传算法（NSGA-III）用于解决多目标优化问题。拟议的模型是针对伊朗的Karkheh水库系统开发的，是一个现实问题。结果表明，在提供有关水库水质状况的足够信息时，优化水质采样位置的性能优于所有可能的采样位置。所提出的基于不确定性的模型可以将由不同情况引起的不确定区间的半径减小55.73％。时空多目标优化问题中的不确定性处理是这项研究的主要贡献，它提供了对各种情况较不敏感的储层监测系统的可靠而可靠的设计。