International Journal of Scientific Research and Engineering Development

Forest fires are a serious environmental issue that result in loss of a large area of natural resources, damaging wildlife habitat, and destruction of homes. In order to prevent forest fires, we need to be able to identify predisposed high-risk conditions that can lead to outbreaks before an actual fire happens. The authors propose a proactive approach to forest fire prediction using meteorological variables such as temperature, wind speed, and relative humidity, as well as calculated Fire Weather Index (FWI) numbers, to assess possible fire hazard conditions. Their focus is on developing a predictive framework based on the use of the XGBoost machine learning algorithm to evaluate weather-related variables in order to provide users with forest fire risk predictions. In addition, using an Intelligent Causal Analysis (ICA) feature within the system, users will obtain a better understanding of what environmental factors have contributed to elevated fire risk levels when evaluating their situation. An Internet-enabled user interface called Forest Guardian Command Center will also be created to provide users with real time monitoring, geospatial observation of threats, and role-dependent access control to the command center for authorized personnel. Additionally, automated SMS and Email notification services will be incorporated into the system to ensure immediate communication with emergency responders when dangerous fire risk conditions are identified. Ultimately, the authors propose that by integrating predictive analytics, explainable insights, and automated emergency notifications into one platform, they will assist users in managing forest fire proactively, and reduce the risk of forest fires starting.

M.Vasuki, G.Hemachandran,"Proactive Forest Fire Prediction and Intelligent Causal Analysis Using Ensemble Machine Learning" International Journal of Scientific Research and Engineering Development, V9(3): Page(1661-1667) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.