Predicting road flooding risk with crowdsourced reports and fine-grained traffic data
Predicting road flooding risk with crowdsourced reports and fine-grained traffic data
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DOI：
10.1007/s43762-023-00082-1
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Faxi Yuan;Cheng-Chun Lee;William Mobley;H. Farahmand;Yuanchang Xu;Russell Blessing;Shangjia Dong;A. Mostafavi;S. Brody
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文献类型：
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作者：
Faxi Yuan;Cheng-Chun Lee;William Mobley;H. Farahmand;Yuanchang Xu;Russell Blessing;Shangjia Dong;A. Mostafavi;S. Brody
本研究的目的是利用机器学习模型，基于地形、水文和时间降水特征预测道路洪水风险。现有的道路淹没研究要么缺乏用于模型验证的经验数据，要么主要侧重于基于洪水图的道路淹没暴露评估。本研究通过使用众包和细粒度交通数据作为道路淹没的指标，并使用地形、水文和时间降水特征作为预测变量，解决了这一限制。然后对两种基于树木的机器学习模型（随机森林和AdaBoost）进行了测试和训练，以预测2017年飓风哈维和2019年热带风暴伊梅尔达在德克萨斯州哈里斯县的道路淹没情况。哈维飓风的研究结果表明，降水是预测道路淹没敏感性的最重要特征，地形特征比水文特征对预测道路淹没敏感性更重要。随机森林和AdaBoost模型的AUC得分相对较高（Harvey分别为0.860和0.810，Imelda分别为0.790和0.720），随机森林模型在这两种情况下的表现都更好。随机森林模型对Harvey表现出稳定的性能，而对Imelda表现出显著的变化。这项研究在道路水平的预测洪水风险测绘方面推进了智能洪水恢复能力这一新兴领域。特别是，这些模型可以帮助受影响的社区和应急管理机构制定更好的准备和应对战略，提高对极端天气事件发生时道路被淹没可能性的态势认识。
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Existing road inundation studies either lack empirical data for model validations or focus mainly on road inundation exposure assessment based on flood maps. This study addresses this limitation by using crowdsourced and fine-grained traffic data as an indicator of road inundation, and topographic, hydrologic, and temporal precipitation features as predictor variables. Two tree-based machine learning models (random forest and AdaBoost) were then tested and trained for predicting road inundations in the contexts of 2017 Hurricane Harvey and 2019 Tropical Storm Imelda in Harris County, Texas. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility, and that topographic features are more critical than hydrologic features for predicting road inundations in both storm cases. The random forest and AdaBoost models had relatively high AUC scores (0.860 and 0.810 for Harvey respectively and 0.790 and 0.720 for Imelda respectively) with the random forest model performing better in both cases. The random forest model showed stable performance for Harvey, while varying significantly for Imelda. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level. In particular, such models could help impacted communities and emergency management agencies develop better preparedness and response strategies with improved situational awareness of road inundation likelihood as an extreme weather event unfolds.