This study proposes an optimization framework based on a hybrid information-entropy approach to identify leakage events in water distribution networks (WDN). Optimization-based methods are widely employed in the literature for such purposes; however, they are constrained by time-consuming procedures. Hence, researchers eliminate parts of the decision space to curtail the computational burden. Here, we propose an information theory-based approach, using Value of Information (VOI) and Transinformation Entropy (TE) methods, in conjunction with an optimization model to explore the entire decision space. VOI allows for the entire feasible space search through intelligent sampling, which in turn ensures robust solutions. TE minimizes redundant information and helps maximize the spatial distribution of sensors. The herein proposed model is developed within a multi-objective optimization framework that renders a set of Pareto-optimal solutions. ELimination and Choice Expressing the REality (ELECTRE) multi-criteria decision-making model is then used to select the best compromise solution given several weighting scenarios. The results of this study show that the information-entropy based scheme can improve the precision of leak detection by enhancing the decision space, and can reduce the computational burden.

这项研究提出了一种基于混合信息熵方法的优化框架，以识别供水网络（WDN）中的泄漏事件。基于优化的方法已在文献中广泛用于此目的。但是，它们受到耗时的过程的限制。因此，研究人员消除了决策空间的一部分，以减轻计算负担。在这里，我们提出了一种基于信息论的方法，该方法使用信息价值（VOI）和转换信息熵（TE）方法以及优化模型来探索整个决策空间。VOI允许通过智能采样进行整个可行的空间搜索，从而确保了可靠的解决方案。TE最大限度地减少了冗余信息，并有助于最大化传感器的空间分布。本文提出的模型是在提供一组帕累托最优解的多目标优化框架内开发的。然后，通过给出真实性的消除和选择（ELECTRE）多准则决策模型，在给定多个加权方案的情况下，选择最佳的折衷解决方案。研究结果表明，基于信息熵的方案可以通过增加决策空间来提高泄漏检测的精度，并可以减轻计算量。