Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA.
Department of Structural, Civil and Environmental Engineering, University at Buffalo, Buffalo, NY, USA.
Risk Anal. 2021 Dec;41(12):2356-2391. doi: 10.1111/risa.13742. Epub 2021 May 30.
Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly right-skewed sewer system failure data to predict the failure risk, leveraging a bidimensional space-time approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodness-of-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.
风险感知资产管理是维护城市下水道系统最佳性能和效率的关键。尽管下水道系统故障具有时空特性，但以前的研究从一维（空间或时间）方面分析故障风险，没有考虑二维时空复杂性。这是由于缺乏高质量的数据，这最终导致了对故障风险的低估/高估。在这里，我们提出了一种通用的方法/框架，以促进对城市下水道系统故障风险的稳健时空分析，克服了数据不完善的内在挑战，例如缺失数据、异常值和不平衡信息。该框架包括一种两阶段的数据驱动建模技术，该技术可有效地对高度右偏的下水道系统故障数据进行建模，以利用二维时空方法预测故障风险。我们对哥伦比亚首都波哥大进行了分析。我们训练、测试和验证了一系列机器学习算法——逻辑回归、决策树、随机森林和 XGBoost，并根据拟合优度和预测准确性选择最佳模型。最后，我们使用最先进的优化技术说明了该框架在规划/安排下水道系统维护操作中的适用性。我们提出的框架可以帮助利益相关者在不同的判别阈值下分析故障风险模型的性能，并为模型的适当空间分辨率和下水道系统维护的分散管理提供管理见解。
