Forest Fire Prediction Using Supervised Machine Learning Models

Abstract

Forest fires are a critical environmental concern, with devastating effects on ecosystems, economies, and human life. Accurate prediction of forest fires is essential for early warning systems and effective resource management. This project explores the use of supervised machine learning techniques, including Random Forest Classifier, Decision Tree Classifier, and Logistic Regression, to predict the occurrence of forest fires based on Fire Weather Index (FWI) components and meteorological parameters.