Geo-Spatial Data Analysis for Disaster Impact Forecasting

This paper explores the application of geo-spatial data analysis in forecasting the impact of natural disasters, focusing on floods and earthquakes. By integrating satellite imagery, geographic information systems (GIS), and machine learning models, the study proposes a framework for predicting disaster-affected areas and estimating potential damages. The methodology leverages high-resolution spatial data and temporal analysis to enhance preparedness and mitigation strategies. A case study on flood forecasting in a hypothetical urban region demonstrates the model accuracy, achieving an F1-score of 0.85. The findings underscore the importance of geo-spatial technologies in disaster management, offering actionable insights for policymakers and emergency responders.