供水管线失效事件预测模型精度研究

Accuracy analysis of water supply pipeline failure prediction models

关键词:  供水管线  漏损事件  失效模型  分类模型  回归模型

Key words:  water supply pipeline  leakage event  failure model  classification model  regression model

摘要:

构建城市供水管线失效事件预测模型,可用于评估管线的失效可能性,是供水管网更新改造的重要依据。供水管线失效模型的建模方法包括分类和回归两类,现有失效模型研究往往采用其中1种方法进行案例分析,缺乏两种建模方法适用性和精度的比较。为此,基于某实例管网数据,采用随机森林（RF）、误差反向传播神经网络（BPNN）和支持向量机（SVM）3种机器学习算法,建立供水管线失效分类模型和回归模型。采用一致性指数（C-index）对比分类与回归模型的准确性,并使用分类指标与回归指标分别分析建模数据集划分方式与构成比例对供水管线失效模型的影响。结果表明:RF构建的失效模型均表现出最好的性能,分类模型的C-index比回归模型相应结果高5.4%~32.8%；与按照年份划分建模数据集的方式相比,随机划分建模数据集能够提升两类模型的预测精度；建模数据集构成比例对两类模型预测精度的影响存在差异,当未失效管线数据占比增大时,分类模型预测管线失效事件的准确度降低,而回归模型预测管线失效时间的误差减小。在实际构建供水管线失效模型时,需要根据对象数据集的特征,合理选择建模方法,并关注数据集的划分方式和构成比例对模型结果的影响。

Abstract:

Constructing a predictive model for urban water supply pipeline failure events is crucial for assessing the likelihood of pipeline failures and serves as an important basis for the renovation and upgrading of water supply networks. The modeling methods for water supply pipeline failure models include classification and regression. Current research on failure models often employs only one of these methods for case analysis, lacking a comparison of the applicability and accuracy of both modeling methods. To address this gap, based on data from a specific instance of a water supply network, this paper establishes water supply pipeline failure classification and regression models using three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network (BPNN), and Support Vector Machine (SVM). The concordance index (C-index) is used to compare the accuracy of the classification and regression models. Additionally, classification and regression indicators are employed to analyze the impact of modeling dataset division, as well as composition ratios of the dataset on the water supply pipeline failure models. The results show that the failure models constructed by RF exhibit the best performance, with the C-index of the classification models being 5.4% to 32.8% higher than that of the corresponding regression models. Compared to dividing the modeling dataset by year, randomly dividing the modeling dataset can enhance the predictive accuracy of both types of models. Furthermore, the impact of the modeling dataset composition ratio on the predictive accuracy of both types of models varies; as the proportion of non-failure pipeline data increases, the accuracy of the classification model in predicting pipeline failure events decreases, while the regression model shows reduced error in predicting pipeline failure times. Therefore, when constructing water supply pipeline failure models in practice, it is necessary to choose the modeling method appropriately based on the characteristics of the target dataset and pay attention to the impact of dataset division methods and composition ratios on the model results.