The importance of drinking water distribution networks (DWDNs) as critical urban infrastructures has led to the development and utilization of models for the analysis, design, operation, and management of DWDNs, to ensure optimal efficiency and water quality. In order to provide models that accurately represent real-world behavior and characteristics of an actual DWDN, model calibration is an essential and crucial procedure (Alves et al., 2014). However, since DWDNs are generally large, underground networks, data availability for model calibration is often an issue. In this paper, we introduce a novel automatic calibration methodology called Expert Systems and Neuro-Evolution of Augmenting Topologies (ES-NEAT). The proposed methodology leverages the power of Expert Systems (ES) and genetic algorithms for the evolution of neural network topologies to efficiently search for the optimal solution of high dimensional calibration problems while maintaining moderate computational effort. One of the key strengths of ES-NEAT lies in its ability to achieve high accuracy even with limited availability of measurements, addressing the inherent uncertainty in real-world DWDNs. By integrating specific knowledge provided by different stakeholders using the ES methodology, the framework offers a flexible approach that adapts to the unique characteristics of each drinking water distribution network. Moreover, the methodology is designed to store calibration information and transfer it in a structured format for use in subsequent calibration processes, increasing efficiency and ensuring generalizability. The method was successfully applied to a benchmark network model as well as a real-case study of a DWDN in Flanders, Belgium.

翻译：饮用水管网作为关键的城市基础设施，其重要性推动了相关模型在管网分析、设计、运行与管理中的开发与应用，以确保管网运行效率与水质的最优化。为使模型能够准确反映实际饮用水管网的真实行为与特性，模型校准是一项至关重要且必不可少的步骤（Alves等人，2014）。然而，由于饮用水管网通常是规模庞大且位于地下的网络系统，用于模型校准的数据可获取性往往成为一大难题。本文提出了一种名为专家系统与增强拓扑神经进化（ES-NEAT）的新型自动校准方法。该方法结合了专家系统的优势与用于神经网络拓扑进化的遗传算法，能够在保持适度计算成本的同时，高效搜索高维校准问题的最优解。ES-NEAT的核心优势之一在于，即使在测量数据有限的情况下，仍能实现高精度校准，从而应对实际饮用水管网中固有的不确定性。通过利用专家系统方法整合不同利益相关方提供的专业知识，该框架提供了一种灵活的方法，能够适应每个饮用水管网的独特特性。此外，该方法设计用于存储校准信息，并以结构化格式进行传输，供后续校准过程使用，从而提高了效率并确保了方法的可推广性。该方法已成功应用于一个基准网络模型以及比利时法兰德斯地区一个实际饮用水管网的案例研究。