Forest Fire Occurrence Prediction Using Machine Learning

Abstract

Forest fires annually devastate vast areas of forest cover, causing extensive damage to flora and fauna, and driving numerous species to extinction. Machine Learning offers a promising avenue for predicting forest fires, potentially enabling proactive measures to safeguard wildlife. This research focuses on predicting forest fire likelihood based on oxygen, temperature, and humidity levels at a given location. The proposed concept involves developing a website that accepts user inputs for these parameters and provides real-time forest fire probability predictions. The study aims to detect and alert forest fire occurrences using dataset-derived temperature, humidity, and oxygen values, culminating in the creation of a web interface for forest fire detection and monitoring.