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@article{171273,
        author = {K. Mathivanan and G. Bavadharani and Dr. R. Nithya},
        title = {Enhancing Forest Fire Prognosticating Using Machine Learning Algorithm – A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {7},
        pages = {4096-4101},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171273},
        abstract = {Forest fires pose significant threats to ecosystems, human health, and economies, making accurate prediction crucial for effective management and mitigation. Traditional fire prediction models often rely on meteorological data, historical fire records, and expert systems, which face limitations in terms of precision and adaptability. Recent advances in machine learning (ML) offer promising solutions by enabling the analysis of large, complex datasets and the recognition of intricate patterns that predict fire occurrence and behavior. This survey paper explores the application of various ML techniques, including supervised (e.g., decision trees, support vector machines), unsupervised (e.g., clustering, PCA), and hybrid models (e.g., ensemble methods, deep reinforcement learning) for enhancing fire prognostication. We examine their effectiveness in fire risk mapping, behavior modeling, and real-time prediction, highlighting both the strengths and challenges. Despite notable progress, issues related to data quality, model interpretability, and scalability persist, suggesting that further research is needed to integrate ML with traditional fire management practices and improve model transparency. This paper also identifies future directions, including the incorporation of real-time data, hybrid ML systems, and cross-disciplinary approaches to create more adaptive and reliable fire prediction tools.},
        keywords = {Machine Learning, Forest Fire Prediction, Forest Fire Fighting, Formation Control, Random Forest, Support Vector Machine.},
        month = {January},
        }