Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems
Integrating spatial clustering with predictive modeling of pipe failures in water distribution systems
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DOI：
10.1080/1573062x.2023.2180393
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发表时间：
2023-02-27
影响因子：
2.7
通讯作者：
Sela, Lina
中科院分区：
文献类型：
--
作者：
Abokifa, Ahmed A.;Sela, Lina
配水基础设施（WDI）中的管道故障具有重大的经济、环境和公共健康影响。为了减轻这些影响，需要优先考虑维修和更换决策，以有效降低故障率。在这项研究中，提出了一个计算框架WDI资产管理，耦合空间聚类分析与预测建模的管道故障。首先，热点/冷点集群的统计上显着的高/低故障率确定使用本地指标的空间关联。其次，8个统计学习技术的预测能力进行了系统的测试，并实施了最佳性能的方法来预测故障率，（休息/（公里。年））在不同部门的WDI。第三，实施该框架，以比较采用主动而不是被动的管道更换策略的影响。将该框架应用于现实生活中，大规模的WDI显示，管道故障的空间聚类提高了预测模型的准确性。
Pipe failures in water distribution infrastructure (WDI) have significant economic, environmental and public health impacts. To alleviate these impacts, repair and replacement decisions need to be prioritized to effectively reduce failure rates. In this study, a computational framework is proposed for WDI asset management that couples spatial clustering analysis with predictive modeling of pipe failures. First, hotspot/coldspot clusters of statistically significant high/low failure rates are identified using local indicators of spatial association. Second, the predictive abilities of eight statistical learning techniques are systematically tested, and the best-performing method is implemented to forecast failure rates,(breaks/(km.year)) within different sectors of the WDI. Third, the framework is implemented to compare the impact of adopting proactive instead of reactive pipe replacement strategies. Applying the framework to a real-life, large-scale WDI revealed that spatial clustering of pipe failures improves the accuracy of the prediction models.