Year: 2025
Type: article
Abstract: 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.
Authors: Rupesh Waghmare, Dr. B. A. Sonkamble -
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411470737