Aging infrastructure and funding problems continue to plague water supply networks worldwide. The failure risk of water mains is posing a considerable threat to drinking water security in urban centers, necessitating performance assessment strategies. The prediction of networks' condition index has seldom been studied, while a novel automated method can be developed to reasonably address this issue, thus promoting sustainability-based strategies. This paper presents an integrated framework for the assessment and quantification of the water main condition index. The Arequipa region in Peru consists of eight provinces; this region is chosen to exemplify the proposed framework due to the rapid pace of urbanization, making water infrastructure even more crucial. The novelty of this study includes innovation of research concept as it restructures the water network condition assessment by exploiting the hybridization technique of two potent intelligent systems; namely, the adaptive neuro-fuzzy inference system and the fuzzy inference system. These systems are employed sequentially to accomplish computational simulations and reasoning consolidations to generate automatically one condition index. Thus, making up for the lack of integrated research in water piping system. This research also gives prominence to enhancing water sustainability via incorporating the physical factors (pipe attributes) together with operational and environmental factors, represented by the impact of leaching and disinfection byproducts on people's health. The neuro-fuzzy processor is designed to predict each province's condition index via the grid partitioning and hybrid algorithm. The hybrid algorithm uses a combination of backpropagation and least-squares regression to optimize and tune the fuzzy parameters. The fuzzy consolidator indicated that the region's network condition index is 63.1, which reveals a medium condition for the Arequipa region water networks. The integrated framework is validated by conducting a comparative analysis with the multiple linear regression model. The results reveal better performance of the proposed framework since it demonstrates higher R² of 0.9145. This study promotes multidimensional applications in urban sustainability, environmental sustainability, and urban water management systems.

老化的基础设施和资金问题继续困扰着全世界的供水网络。水管的故障风险对城市中心的饮用水安全构成了相当大的威胁，因此需要制定绩效评估策略。网络状况指数的预测很少被研究，而可以开发一种新的自动化方法来合理地解决这个问题，从而促进基于可持续性的策略。本文提出了一个用于评估和量化水主条件指标的综合框架。秘鲁的阿雷基帕地区由八个省组成；由于城市化的快速步伐，使水基础设施变得更加重要，因此选择该地区作为拟议框架的例证。本研究的创新之处在于研究理念的创新，利用两个强大的智能系统的混合技术重构了水网状况评估；即自适应神经模糊推理系统和模糊推理系统。这些系统被依次采用来完成计算模拟和推理整合，以自动生成一个条件指标。从而弥补了水管系统综合研究的不足。该研究还突出强调了通过将物理因素（管道属性）与操作和环境因素（以浸出和消毒副产品对人们健康的影响为代表）相结合来提高水的可持续性。神经模糊处理器旨在预测每个省 s 条件索引通过网格划分和混合算法。混合算法使用反向传播和最小二乘回归的组合来优化和调整模糊参数。模糊合并器表明该地区的网络状况指数为 63.1，这表明阿雷基帕地区水网络处于中等状况。通过与多元线性回归模型进行比较分析来验证集成框架。结果表明所提出的框架具有更好的性能，因为它展示了更高的 R 这揭示了阿雷基帕地区供水网络的中等条件。通过与多元线性回归模型进行比较分析来验证集成框架。结果表明所提出的框架具有更好的性能，因为它展示了更高的 R 这揭示了阿雷基帕地区供水网络的中等条件。通过与多元线性回归模型进行比较分析来验证集成框架。结果表明所提出的框架具有更好的性能，因为它展示了更高的 R0.9145 中的²。这项研究促进了城市可持续性、环境可持续性和城市水资源管理系统的多维应用。