Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk
Seyed M. H. S. Rezvani, Alexandre Gonçalves, Maria João Falcão Silva, N. Almeida
Abstract
• Developed Geospatial AI (machine learning) model to create flood hazard maps. • Produced flood hazard maps identifying potential hotspots at city levels. • Processed data at 1 km x 1 km, adapting TIFF maps for ML model compatibility. • Applied flood risk factors: DEM, slope, permeability, texture, river/sea proximity. • Integrated topographic and land attributes data to identify intrinsic flood risk. This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.