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首页> 外文期刊>Journal of Infrastructure Systems >Prioritizing Sanitary Sewers for Rehabilitation Using Least-Cost Classifiers
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Prioritizing Sanitary Sewers for Rehabilitation Using Least-Cost Classifiers

机译：使用成本最低的分类器对卫生下水道进行修复的优先级

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摘要

Sewer rehabilitation is one control option for relieving wet-weather sanitary sewer overflows by reducing rainfall dependent inflow and infiltration. The prioritization of rehabilitation activities is based on the physical condition of a collection system, which is usually only partially known. Linear regression methods have been used to estimate the condition of the unobserved set of pipes based on relationships derived from the observed set of pipes. This method can provide unsatisfactory relationships between the sewer condition and measured independent variables, resulting in highly uncertain rehabilitation cost estimates. Discriminant analysis has been effective at weighing the costs of misclassifying pipes and deriving least-cost classification rules. Classification rules may be derived within a data-mining framework using evolutionary algorithms or with logistic regression methods. The estimated spatial distribution of deficient pipes may then be refined on an aggregated scale with a bootstrap estimate of classification error. The result is a screening and prioritization tool for providing cost estimates for rehabilitation and replacement activities. This method is demonstrated with an 8,919 pipe sanitary sewer system in Vallejo, Calif.
机译：下水道修复是通过减少降雨引起的入流和入渗来缓解雨天卫生下水道溢流的一种控制选择。康复活动的优先级基于收集系统的物理状况，通常只能部分了解。线性回归方法已用于基于从观察到的管道组得出的关系来估计未观察到的管道组的状况。该方法可能会在下水道状况和测得的独立变量之间提供不令人满意的关系，从而导致高度不确定的修复成本估算。判别分析有效地权衡了管道错误分类的成本并得出了成本最低的分类规则。分类规则可以在数据挖掘框架内使用进化算法或逻辑回归方法得出。不足的管道的估计空间分布然后可以用分类误差的自举估计在汇总规模上进行细化。结果是一个筛选和确定优先次序的工具，用于提供修复和替换活动的成本估算。加利福尼亚州瓦列霍的8,919管道下水道系统演示了此方法。

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