DONGYiguang,WANGDan,HUANGMengni,et al.Risk Prediction of Drainage Pipelines Based on Logistic Regression Model[J].China Water & Wastewater,2026,42(12):71-78.
基于逻辑回归模型的排水管道风险预测
- Title:
- Risk Prediction of Drainage Pipelines Based on Logistic Regression Model
- 关键词:
- 排水管道; 风险预测; Logistic回归; 管道缺陷; 地理信息系统
- Keywords:
- drainage pipeline; risk prediction; Logistic regression; pipeline defect; geographic information system (GIS)
- 摘要:
- 近年来，地下排水管网问题频发，对水环境和公共财产构成严重威胁。开展排水管网风险预测对于优化管网运维与修复计划、提升运行安全性具有重要意义。深圳市地下管网检测维护数据完整，为精确预测风险提供了数据基础。同时，地理信息系统（GIS）与机器学习的发展，为管网风险预测提供了新的研究视角。以国内某区域的实测管网数据为研究对象，选取80%的风险点作为训练数据，结合雨/污水、管龄、管径、管长、管材、标高和埋深等7个风险因子，采用二元Logistic回归模型进行风险预测，并通过混淆矩阵与受试者工作特征（ROC）曲线评价模型性能。研究结果显示：①污水管道、管龄较大及管径较小的管道呈现出显著的高风险特征；②模型的总体准确率为79.0%，训练集与验证集的曲线下面积（AUC）值分别为80.1%和84.6%，表明其预测能力较强；③风险概率等级为三、四级的管段占27.33%，覆盖研究区内61.90%的风险点，验证了预测结果与实际分布的一致性。研究表明，基于多维风险因子与Logistic回归模型的风险预测方法能够有效评估研究区排水管网的风险分布，为管网的科学管理与优化决策提供可靠支持。
- Abstract:
- In recent years, frequent issues in underground drainage networks have posed serious threats to aquatic environments and public assets. Conducting risk prediction for drainage networks holds significant importance for optimizing pipeline maintenance and rehabilitation plans, as well as enhancing operational safety. The complete detection and maintenance data of underground pipelines in Shenzhen provide a robust foundation for precise risk prediction. Meanwhile, advancements in geographic information system (GIS) and machine learning offer new research perspectives for pipeline risk assessment. This study utilizes measured pipeline data from a district in China, selecting 80% of risk points as training data. Seven risk factors include sewage/stormwater pipeline type, pipe age, diameter, length, material, elevation, and burial depth. These factors are incorporated into a binary Logistic regression model for risk prediction. Model performance is evaluated using a confusion matrix and ROC curve. The results demonstrate: ① Sewage pipelines, aging pipes, and smaller-diameter pipes exhibit significantly high-risk characteristics; ② The model achieves an overall accuracy of 79.0%, with AUC values of 80.1% and 84.6% for the training and validation sets, respectively, indicating strong predictive capability; ③ Pipe segments with risk probability grades Ⅲ and Ⅳ accounted for 27.33% of cases, covering 61.90% of risk points in the study area, which aligns with the actual risk distribution. This research confirms that the risk prediction method, integrating multi-dimensional risk factors and Logistic regression modeling, effectively evaluates the risk distribution of drainage networks in the study area, providing reliable support for scientific management and decision-making in drainage pipeline maintenance.
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