Enhancing Fluvial Modeling and Flood Prediction Accuracy through the Fusion of 3D Point Clouds, Multispectral, and RGB Data

Abstract

Floods are among the most prevalent and devastating natural disasters, affecting over 2 billion people worldwide. The increasing frequency and intensity of extreme precipitation events, driven by climate change, increase the vulnerability of communities to flooding. This underscores the urgent need for more accurate and reliable flood prediction and risk assessment models to enhance preparedness and mitigation efforts. In this research, we investigate the critical role of Digital Elevation Model (DEM) resolution in fluvial flood modeling. DEMs are essential for representing the Earth’s surface and are necessary in hydrodynamic simulations used to predict flood behavior. We employed a two-dimensional hydrodynamic model to simulate flood scenarios using DEMs of three different resolutions: 1 m, 5 m, and 25 m. The objective was to determine how variations in DEM resolution influence the accuracy of flood extent predictions. Our findings reveal that higher resolution DEMs, such as those with 1-meter resolution, provide a more precise and detailed representation of the terrain. This level of detail significantly reduces the predicted extent of flooding compared to lower resolution DEMs (5m and 25 m).