Pre-disaster planning and mitigation necessitate detailed spatial information about flood hazards and their associated risks. In the US, the Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) provides important information about areas subject to flooding during the 1 % riverine or coastal event. The binary nature of flood hazard maps obscures the distribution of property risk inside of the SFHA and the residual risk outside of the SFHA, which can undermine mitigation efforts. Machine learning techniques provide an alternative approach to estimating flood hazards across large spatial scales at low computational expense. This study presents a pilot study for the Texas Gulf Coast region using random forest classification to predict flood probability across a 30 523 km² area. Using a record of National Flood Insurance Program (NFIP) claims dating back to 1976 and high-resolution geospatial data, we generate a continuous flood hazard map for 12 US Geological Survey (USGS) eight-digit hydrologic unit code (HUC) watersheds. Results indicate that the random forest model predicts flooding with a high sensitivity (area under the curve, AUC: 0.895), especially compared to the existing FEMA regulatory floodplain. Our model identifies 649 000 structures with at least a 1 % annual chance of flooding, roughly 3 times more than are currently identified by FEMA as flood-prone.

Rescue requests during large‐scale urban flood disasters can be difficult to validate and prioritise. High‐resolution aerial imagery is often unavailable or lacks the necessary geographic extent, making it difficult to obtain real‐time information about where flooding is occurring. In this paper, we present a novel approach to map the extent of urban flooding in Harris County, Texas during Hurricane Harvey (August 25–30, 2017) and identify where people were most likely to need immediate emergency assistance. Using Maximum Entropy, we predict the probability of flooding based on several spatially‐distributed physical and socio‐economic characteristics coupled with crowdsourced data. We compare the results against two alternative flood datasets available after Hurricane Harvey (i.e., Copernicus satellite imagery and riverine flood depths estimated by FEMA), and we validate the performance of the model using a 15% subset of the rescue requests, Houston 311 flood calls, and inundated roadways. We find that the model predicts a much larger area of flooding than was shown by either Copernicus or FEMA when compared against the locations of rescue requests, and that it performs well using both a subset of rescue requests (AUC 0.917) and 311 calls (AUC 0.929) but is less sensitive to inundated roads (AUC 0.721).

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.

Large, complex coastal regions often require a combination of interventions to lower the risk of flooding to an acceptable level. In practice, a limited number of strategies are considered and interdependencies between interventions are often simplified. This paper presents the Multiple Lines of Defence Optimization System (MODOS)‐model. This quick, probabilistic model simulates and evaluates the impact of many flood risk reduction strategies while accounting for interdependencies amongst measures. The simulation includes hydraulic calculations, damage calculations, and the effects of measures for various return periods. The application and potential of this model is shown with a conceptual and simplified case study, based on the Houston‐Galveston Bay area. The analyses demonstrate how the MODOS‐model identifies trade‐offs within the system and shows how flood risk, cost, and impact respond to flood management decisions. This improved understanding of the impact of design and planning choices can benefit the discussions in finding the optimal flood risk reduction strategy for coastal regions.

Adopting effective flood mitigation practices for repetitive flood events in the United States continues to play a prominent role in preventing future damage and fostering resilience to residential flooding. Two common mitigation practices for reducing residential flood risk consist of raising an existing structure to or above base flood elevation (BFE) and acquiring chronically damaged properties in flood prone areas and restoring them back to serve their natural functions as green open spaces. However, due to data accuracy limitations, decision makers are faced with the challenge of identifying the financially optimal approach to implementing mitigation measures. We address this problem through the following research questions: What does the optimal allocation of flood mitigation resources look like under data uncertainty, and what are the optimal methods to combining mitigation measures with consideration for the best economic benefits? Using a robust decision making (RDM) approach, the effects of uncertainty in property values, construction and demolition costs, and policy implementation options such as structure selection and budget allocation were measured. Our results indicate that the amount budgeted for mitigation and how those funds are allocated directly influence the selection of the most economically viable mitigation practices. Our research also contributes to the growing need for evaluating specific flood mitigation strategies.

Researchers have a limited understanding of the interactions between development patterns and Ignition Probability. This study explores the variation in Ignition Probability as a result of differing development patterns. Based on LANDFIRE datasets, development changes were mapped for two sets of years (2001 and 2012) and the relationship between development and Ignition Probability was assessed. The study area covered the two adjacent counties, Bastrop and Travis located in Texas, USA. These two counties have a high potential for wildfire, and due to expanding development have high vulnerability. Expanding lateral development was organized into one of five categories: infill, radial, isolated, clustered, and linear. The Maximum Entropy algorithm predicted the spatial distribution of ignition probabilities based on several physical and land use characteristics coupled with historic ignition locations. Variation in Ignition Probability was assessed for each category of development using two-way ANOVA’s and post hoc analysis. Ignition Probability maps indicated a fair sensitivity (Area Under the Curve: 0.77–0.78), suggesting that the spatial configuration of development patterns influenced wildfire ignition. Analyses found that isolated, clustered, and linear outlying development patterns had higher Ignition Probability than infill and radial development, and that fire probabilities fell along a development gradient. This trend between the development gradient and ignition probabilities should be used to direct land use planning to reduce fire risk.