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首页> 外文期刊>Journal of Hydroinformatics >Comparing the utility of decision trees and support vector machines when planning inspections of linear sewer infrastructure
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Comparing the utility of decision trees and support vector machines when planning inspections of linear sewer infrastructure

机译：在计划检查线性下水道基础设施时，比较决策树和支持向量机的效用

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摘要

Closed-circuit television inspection technology is traditionally used to identify aging sewer pipes requiring rehabilitation. While these inspections provide essential information on the condition of pipes hidden from day-to-day view, they are expensive and often limited to small portions of an entire sewer system. Municipalities may benefit from utilizing predictive analytics to leverage existing inspection datasets so that reliable predictions of condition are available for pipes that have not yet been inspected. The predictive capabilities of data mining systems, namely support vector machines (SVMs) and decision tree classifiers, are demonstrated using a case study of sanitary sewer pipe inspection data collected by the municipality,of Guelph, Ontario, Canada. The modeling algorithms are implemented using open-source software and are tuned to counteract the negative impact on predictive performance resulting from class imbalance common within pipe inspection datasets. The decision tree classifier outperforms SVM for this classification task - achieving an acceptable area under the receiver operating characteristic curve of 0.77 and an overall accuracy of 76% on a stratified test set. Although predicting individual pipe condition is a notoriously difficult task, decision trees are found to be a useful screening tool for planning future inspection-related activities.
机译：传统上，闭路电视检查技术用于识别需要修复的老化的下水道。尽管这些检查提供了日常视图中隐藏的管道状况的基本信息，但它们很昂贵，并且通常仅限于整个下水道系统的一小部分。市政当局可能会受益于利用预测分析来利用现有的检查数据集，以便对尚未检查的管道提供可靠的状态预测。数据挖掘系统的预测能力，即支持向量机（SVM）和决策树分类器，是通过加拿大安大略省圭尔夫市市政卫生污水管道检查数据的案例研究证明的。建模算法使用开源软件实现，并进行了调整，以抵消管道检查数据集中常见的类不平衡对预测性能的负面影响。决策树分类器在分类任务上的性能优于SVM-在分层测试集上，接收器工作特性曲线下的可接受面积为0.77，总体精度为76％。尽管预测单个管道的状态是一项非常困难的任务，但发现决策树是用于计划未来检查相关活动的有用筛选工具。

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