It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.

中文翻译：

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运用多元回归分析确定供水系统中水损失的有效因素

通过智能水技术管理供水系统中的泄漏非常重要。为了减少水的流失，正在积极研究影响非收益水（NRW）的水管网的主要因素。近年来，已经进行了使用统计分析技术（例如人工神经网络（ANN）和主成分分析（PCA））来估计NRW的研究。目前正在积极研究确定影响目标地区北威州的因素。在这项研究中，通过多元回归分析选择的主成分被重新分类，并使用PCA-ANN应用于NRW估算。结果表明，通过PCA估算的主成分与使用ANN的NRW估算相关。通过研究提出的详细的NRW估算方法，