Early wildfire detection using different machine learning algorithms

Abstract

Early detection of wildfires is essential for mitigating their impact on forests and surrounding areas. In this study, we propose a wireless sensor node system that combines multiple low-cost sensors with an artificial intelligence-based detection method for early wildfire detection. The system architecture includes temperature, humidity, and smoke sensors, as well as a wireless communication module. Four machine learning classifiers, including decision trees, random forests, support vector machines, and k-nearest neighbors, were evaluated for their effectiveness in predicting wildfire detection using a dataset collected in a forest area. The results showed that the random forest algorithm with optimum hyperparameters had the highest accuracy in classifying fire and non-fire samples (77.95% correctly classified). The proposed system provides an effective and cost-efficient solution for early wildfire detection in large forest areas.