ALTIMETRIC MODEL OBTAINED BY GNSS, TOPOGRAPHY AND RPA FOR URBAN FLOOD MANAGEMENT: CASE STUDY IN THE PORTO NEIGHBORHOOD, PELOTAS-RS

Abstract

Rapid urbanization and climate change have intensified urban floods and inundations, demanding effective tools for risk analysis and mitigation. Soil impermeabilization increases cities' susceptibility to extreme hydrological events. This study applies geotechnologies to identify critical flood risk areas in the Porto neighborhood, Pelotas-RS, using remote sensing and Geographic Information Systems (GIS). The methodology integrates high-resolution aerial photogrammetric images captured by Remotely Piloted Aircraft Systems (RPAS) and precise planialtimetric data obtained through Global Navigation Satellite System (GNSS) and topographic surveys, generating a georeferenced orthomosaic and a detailed Digital Elevation Model (DEM). A rigorous cartographic accuracy assessment was performed, along with an in-depth flood susceptibility analysis based on topographic parameters and terrain characteristics. The results revealed altimetric variations of approximately one meter within the urban fabric and identified highly flood-prone areas, enabling risk zone classification. A complementary bibliometric review highlighted the growing relevance of machine learning and hydrodynamic modeling in urban risk prediction and management, revealing promising methodological trends. The study concludes that the systematic integration of geotechnologies enables precise identification of critical areas, contributing to effective public policies for risk management and sustainable urban planning, aligning with the Sustainable Development Goals (SDGs) related to water management, resilient urban development, and environmental protection.