Paper Contents
Abstract
Natural disasters pose great risks to human life, property, and the environment worldwide. Early detection and efficientprediction are fundamental for disaster prevention and control. This study introduces a unified framework for natural scene classification and detection of natural disasters using high-level data analytics and machine learning principles. By leveraging real-time input from a variety of sources, including data from weather stations and social media, the system examines intricate patterns and anomalies that could indicate events such as floods, earthquakes, hurricanes, and wildfires. Machine learning models, including deep learning architectures, are trained on historical and live datasets to enhance the prediction accuracy and enable rapid classification of disaster types.
Copyright
Copyright © 2025 Shreyash Ghogare. This is an open access article distributed under the Creative Commons Attribution License.
