Leveraging Satellite Imagery And Hydrological Models For Flood Forecasting And Impact Analysis

Abstract

Flood forecasting and post-flood evaluation is crucial in reducing the disastrous effects of floods, which are now further exacerbated by climate change and urbanization. This article suggests a cutting-edge methodology through the utilization of machine learning, specifically the Random Forest algorithm, to improve flood forecasting and impact assessment accuracy and reliability. Merging various datasets of meteorological, hydrological, and satellite-based geospatial parameters, the method prioritizes extensive data preprocessing, feature extraction, and temporal analysis to forecast flood hazards and estimate post-event effects. The methodology integrates real-time monitoring, interactive visual analytical tools, and alerting services, providing actionable information for disaster preparedness as well as management. The Proposed system is tested for validity using Kozhikode flood-prone area data, with the purpose of demonstrating its flexibility, scalability, and ability to revolutionize flood management strategies. By overcoming problems related to data heterogeneity and computational efficiency, the paper emphasizes the harmony between sophisticated algorithms and real-time data fusion, opening up prospects for stronger and proactive disaster management measures.