An integrated internet of things and machine learning framework for real-time wildfire monitoring and prevention

Forest fires are among the most destructive natural hazards, posing significant threats to ecosystems, infrastructure, and human life. In Mediterranean regions such as Algeria, the frequency and intensity of wildfires have increased due to climate change and human activities. This article proposes a cloud-centric hybrid Internet of Things (IoT) and machine learning (ML) framework for intelligent forest fire monitoring and prevention. The proposed system integrates distributed IoT sensor nodes equipped with temperature and humidity sensors that continuously collect environmental data and transmit them to a central gateway through NRF24L01 communication modules, while long-range communication with the cloud platform is achieved using a SIM808 cellular module. To enhance predictive capabilities, the framework combines real-time IoT sensing data with complementary meteorological variables, including wind speed and rainfall, obtained from external meteorological services during dataset construction. Six supervised ML models—logistic regression, decision tree, random forest, XGBoost, LightGBM, and CatBoost—were evaluated using historical Algerian wildfire data (2000–2003) together with a recent dataset collected in 2024. Experimental results show that XGBoost achieved the highest overall predictive performance with a test accuracy of 98.75% and an F1-score of 98.97%, while Random Forest and CatBoost also demonstrated robust and stable performance. Logistic Regression achieved competitive results with significantly lower computational cost, making it suitable for resource-constrained IoT environments. The proposed hybrid IoT–ML framework enables early wildfire risk assessment and supports proactive decision-making for forest management. These findings demonstrate the potential of integrating IoT sensing, meteorological information, and machine learning to support sustainable environmental monitoring within ambient intelligence systems.