Forest Fire Detection using Machine Learning

Abstract

Forest fires pose a significant threat to ecosystems, biodiversity, and human life, with increasing frequency and intensity due to climate change. Early detection and accurate prediction are critical for effective response and mitigation. This paper presents a machine learning-based approach to forest fire detection and risk prediction using environmental data such as temperature, humidity, wind speed, and rainfall. Various classification algorithms, including Random Forest, Support Vector Machine (SVM), and Logistic Regression, were evaluated to classify fire-prone conditions. The model was trained and tested on publicly available datasets and achieved high accuracy and reliability in predicting potential fire outbreaks. Additionally, feature importance analysis highlighted the key factors influencing fire risk. The proposed system can be integrated with IoT sensors or satellite data feeds to enable real-time monitoring and early warning systems. The results demonstrate that machine learning techniques offer a robust, scalable, and cost-effective solution for forest fire management and can significantly enhance proactive disaster response strategies.