Forest Fire Prediction Using Machine Learning Techniques

Abstract

Forest fires pose a significant threat to ecosystems, biodiversity, and human livelihoods worldwide. Early detection and accurate prediction of forest fires are essential for timely intervention and mitigation efforts. In recent years, machine learning techniques have emerged as powerful tools for forest fire prediction, leveraging diverse data sources and advanced algorithms to improve predictive accuracy. This study explores the application of machine learning techniques in forest fire prediction, utilizing features such as weather conditions, topography, vegetation density, and historical fire data. Various machine learning models, including decision trees, random forests, support vector machines, and neural networks, are employed to develop predictive models capable of forecasting forest fire occurrence and severity. The study evaluates the performance of these models using metrics such as accuracy, precision, recall, and F1-score, and compares their effectiveness in different environmental settings. Additionally, the research investigates the integration of remote sensing data and real-time sensor networks for enhancing the spatial and temporal resolution of forest fire prediction models. Through comprehensive experimentation and validation, this study aims to contribute to the development of robust and reliable systems for forest fire prediction, ultimately aiding in the preservation of natural ecosystems and the protection of human lives and property.