Search for: All records

This study uses mobility data in the context of 2017 Hurricane Harvey in Harris County to examine the impact of flooding on access to dialysis centers. We examined access dimensions using static and dynamic metrics. The static metric is the shortest distance from census block groups to the closest centers. Dynamic metrics are: 1) redundancy (daily unique number of centers visited), 2) frequency (daily number of visits to dialysis centers), and 3) proximity (visits weighted by distance to dialysis centers). The results show that: the extent of dependence of regions on dialysis centers varies; flooding significantly reduces access redundancy and frequency of dialysis centers; regions with a greater minority percentage and lower household income were likely to experience extensive disruptions; high-income regions more quickly revert to pre-disaster levels; larger centers located in non-flooded areas are critical to absorbing the unmet demand from disrupted facilities. 

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.