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@article{162918,
        author = {SHIHAM ONGALLUR and MS.SUMI.M},
        title = {SMART FIREWATCH : INTEGRATING IOT AND MACHINE LEARNING FOR FOREST FIRE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {449-452},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162918},
        abstract = {This paper presents a novel approach to forest fire prediction leveraging the integration of Internet of Things (IoT) devices and machine learning techniques. With the increasing frequency and severity of wildfires, early detection and prediction are paramount for effective mitigation efforts. Our proposed system employs a network of IoT sensors strategically deployed in forested areas to monitor environmental variables such as temperature, humidity, and wind speed. Data collected by these sensors are fed into machine learning algorithms, specifically designed to analyse historical patterns and real- time inputs to predict the likelihood and spread of forest fires. The integration of IoT and machine learning enables proactive measures, such as early warning alerts and resource allocation, to minimize the devastating impact of wildfires on ecosystems and communities. Through simulations and real- world deployments, our system demonstrates promising results in enhancing forest fire prediction accuracy and response efficiency.},
        keywords = {Early Detection, Firewatch, Fire prevention IoT, Machine Llearning, Sensor, Networking.},
        month = {},
        }