Bayesian network based evaluation and comparison of the urban flood risk factors for the 2016 flood and a 100-year return period flood event in Baton Rouge, Louisiana

The study focuses on evaluating and comparing different flood risk factors that correlate with each other and affect the probability of flooding. Previous research is limited to identifying these factors’ influence on specific flood events. In contrast, buildings are constructed based on design flood maps, such as the 100/500-year return period flood map in the United States. Therefore, it is important to compare risk factors obtained from historical events and flood maps to identify any missing flood risk factors. To this end, a study was conducted to determine the difference between the probability of flooding and associated factors from a historic 2016 flood event in Baton Rouge, Louisiana, with the 100-year return period Federal Emergency Management Agency (FEMA)  flood map using a Bayesian network. The Bayesian network approach was used for this study due to its transparent forward and backward inference capabilities. The potential flood risk factors (population, household income, land cover, race, rainfall, river, and road proximity, and topography) were identified and corresponding data was preprocessed in ArcGIS to convert them as raster files of the same extent, and coordinate system. The factors were also classified based on different approaches (i.e., equalization, percentile, k-means clustering, etc.) to identify the most suitable classification method. A likelihood maximization-based parameter learning approach was used to obtain the conditional probability tables in the Bayesian network. This approach was used to develop separate Bayesian networks for the 2016 flood and the 100-year flood map. After setting up the Bayesian networks, sensitivity analysis, influential strength, and correlation matrix were generated, which were used to identify the most important flood risk factors. E.g., it was observed that land cover,topography, and river proximity are highly influential to the probability of flooding.