Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors
Machine learning based water pipe failure prediction: The effects of engineering, geology, climate and socio-economic factors
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DOI：
10.1016/j.ress.2021.108185
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发表时间：
2022-03-01
影响因子：
8.1
通讯作者：
Yu, Xiong (Bill)
中科院分区：
文献类型：
--
作者：
Fan, Xudong;Wang, Xiaowei;Yu, Xiong (Bill)
地下水管在各种物理、机械、环境和社会因素的影响下恶化。可靠的管道故障预测是至关重要的供水网络（WSN）的主动管理策略，这是具有挑战性的传统的基于物理的模型。该研究应用数据驱动的机器学习（ML）模型，通过利用大型供水网络的历史维护数据遗产来预测水管故障。首先建立了多源数据聚合框架，以整合地下管道劣化的各种影响因素。该框架定义了各种数据源的整合标准，包括历史管道破裂数据集、土壤类型数据集、地形数据集、人口普查数据集和气候数据集。在此基础上，开发了LightGBM、人工神经网络、Logistic回归、K-近邻和支持向量分类等5种最大似然算法用于管道失效预测。发现LightGBM实现了最佳性能。分析了水管失效主要影响因素的相对重要性。有趣的是，人们发现社区的社会经济因素会影响管道故障的可能性。这项研究表明，数据驱动的分析，集成了机器学习（ML）技术和建议的数据集成框架有可能支持可靠的决策在无线传感器网络管理。
Underground water pipes deteriorate under the influence of various physical, mechanical, environmental, and social factors. Reliable pipe failure prediction is essential for a proactive management strategy of the water supply network (WSN), which is challenging for the conventional physics-based model. This study applied data-driven machine learning (ML) models to predict water pipe failures by leveraging the historical maintenance data heritage of a large water supply network. A multi-source data-aggregation framework was firstly established to integrate various contributing factors to underground pipe deterioration. The framework defined criteria for the integration of various data sources including the historical pipe break dataset, soil type dataset, topographical dataset, census dataset, and climate dataset. Based on the data, five ML algorithms, including LightGBM, Artificial Neural Network, Logistic Regression, K-Nearest Neighbors, and Support Vector Classification are developed for pipe failure prediction. LightGBM was found to achieve the best performance. The relative importance of major contributing factors on the water pipe failures was analyzed. Interestingly, the socioeconomic factors of a community are found to affect the probability of pipe failures. This study indicates that data-driven analysis that integrates the Machine Learning (ML) techniques and the proposed data integration framework has the potential to support reliable decision-making in WSN management.