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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Coupling of logistic regression analysis and local search methods for characterization of water distribution system contaminant source
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Coupling of logistic regression analysis and local search methods for characterization of water distribution system contaminant source

机译：Logistic回归分析与局部搜索方法的结合，用于水分配系统污染物源的表征

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Accidental or intentional drinking water contamination has long been and remains a major threat to water security throughout the world. An inverse problem can be constructed, given sensor measurements in a water distribution system (WDS), to identify the contaminant source characteristics by integrating a WDS simulation model with an optimization method. However, this approach requires numerous compute-intensive simulation runs to evaluate potential solutions; thus, determining the best source characteristic within a reasonable computational time is challenging. In this paper, we describe the development of a WDS contamination characterization algorithm by coupling a statistical model with a heuristic search method. The statistical model is used to identify potential source locations of contamination and a local search aims at further refining contaminant source characteristics. Application of the proposed approach to two illustrative example water distribution networks demonstrates its capability of adaptively discovering contaminant source characteristics as well as evaluating the degree of non-uniqueness of solutions. The results also showed that the local search as an optimizer has better performance than a standard evolutionary algorithm (EA).
机译：长期以来，偶然或故意的饮用水污染一直是并且仍然是全世界水安全的主要威胁。给定水分配系统（WDS）中的传感器测量值，可以构造一个反问题，以通过将WDS仿真模型与优化方法集成来识别污染物源特征。但是，这种方法需要大量的计算密集型仿真来评估潜在的解决方案。因此，在合理的计算时间内确定最佳光源特性具有挑战性。在本文中，我们通过将统计模型与启发式搜索方法相结合来描述WDS污染表征算法的开发。统计模型用于识别潜在的污染源位置，本地搜索旨在进一步完善污染源特征。所提出的方法在两个示例性示例水分配网络中的应用证明了其能够自适应地发现污染物源特征以及评估溶液的非唯一性程度的能力。结果还表明，与标准进化算法（EA）相比，作为优化程序的本地搜索具有更好的性能。

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