Flood Resilience Through Advanced Wetland Prediction

As climate change intensifies the severity of extreme weather, harnessing the protective functions of wetlands becomes increasingly imperative. The southeastern United States, particularly North Carolina, is highly endowed with different wetland classes that act as natural buffers during natural disasters or storms such as Hurricane Matthew and Hurricane Florence in 2016 and 2018 respectively. This research addresses the delineation of the wetland boundaries after Hurricane Florence, emphasizing the pivotal role of wetlands in flood resilience. Building on the Wetland Intrinsic Potential (WIP) tool, the paper employs machine learning to map and delineate wetlands in Southern North Carolina, focusing on Bladen and Wilmington counties. The study integrates LiDAR data, Sentinel-2 imagery, and the National Wetlands Inventory, utilizing hydrographic, imagery, and topographic inputs for accurate wetland mapping. Results showcase high accuracy in predicting wetland and upland locations, contributing to sustainable flood management practices. The research provides valuable insights into the application of machine learning tools, such as WIP, for wetland mapping and flood mitigation in vulnerable regions.