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@article{191788,
        author = {Ishita Sagar},
        title = {Inferno Intelligence: A Review on AI in Fire Detection and Prevention},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {12},
        number = {no},
        pages = {121-126},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191788},
        abstract = {Forest fires have become one of the most severe environmental threats intensified by climate change, causing extensive damage to ecosystem, human settlements, and the global carbon balance. According to NASA, over 10 million hectares of land are affected by wildfires each year, highlighting the urgent need for predictive and preventive strategies. This review synthesizes recent research from 2019 to 2025 on the application of Artificial Intelligence (AI) in forest fire detection and prevention. It explores how machine learning and deep learning algorithms-such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest-analyze satellite imagery, sensor data, and meteorological patterns to identify early fire signals.
Furthermore, the study examines the integration of AI with Internet of Things (IoT) devices and remote sensing for real-time monitoring and decision-making. While these technologies significantly improves detection accuracy and response speed, challenges persist in data reliability, cost, and model scalability. Overall, the paper concludes that AI-based systems hold transformative, automated forest management solutions that can effectively reduce the frequency and impact of wildfires.},
        keywords = {Artificial Intelligence, Deep Learning, Forest Fire Detection, IoT, Machine Learning, Remote Sensing.},
        month = {},
        }