Seasonal Forest Disaster Prediction using Machine Learning Models

The forests are natural barriers that help prevent natural disasters such as heavy rainfall, heavy winds, etc. Because these disasters affect the forest, they make way for more disasters in surrounding areas including other forests, affecting animals, cities, and villages, especially tribal populations. This project aims to find solutions by analyzing the forest and its historical data including disaster incidents. The Seasonal Forest Disaster Analysis project harnesses advanced Machine Learning to investigate and mitigate the impact of forest disasters comprehensively. In essence, the Seasonal Forest Disaster Analysis project stands as a testament to the potential of cutting-edge technology and collaborative efforts in tackling some of the most pressing challenges facing our natural world. The Seasonal Forest Disaster Analysis project is a comprehensive endeavor that harnesses advanced Machine Learning & Deep Learning techniques to deeply investigate and mitigate the impact of forest disasters. Three models like CNN with Grad-CAM, ResNet and XceptionNet were proposed, out of which CNN with Grad-CAM produces an Accuracy of 94 %. By integrating a diverse array of datasets, including satellite imagery, climate data, and historical records, the project is geared towards effectively identifying, categorizing, and predicting a spectrum of seasonal forest disasters, including but not limited to wildfires, droughts, and floods. This integration enables the creation of a sophisticated framework that not only examines past occurrences but also foresees potential future hazards through predictive modelling.