  The objective of this study is to predict road flooding risks based on
topographic, hydrologic, and temporal precipitation features using machine
learning models. Predictive flood monitoring of road network flooding status
plays an essential role in community hazard mitigation, preparedness, and
response activities. Existing studies related to the estimation of road
inundations either lack observed road inundation 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
essential 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. For
example, 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.
