Secured Forest Fire Prediction Using Blockchain and CNN Transformers

Forest fires are among the worst natural disasters in recent years, causing many deaths, serious injuries, and significant damage to economic and environmental resources. Various methods have been proposed for the early detection of forest fire areas, including the use of meteorological data. However, none of these methods provide real-time image processing that extract significant spatial and temporal features to detect forest fire areas with high accuracy. Additionally, forest fire images exposed to unsafe public networks are vulnerable to security threats and attacks. This paper aims to predict forest fires using advanced Artificial Intelligence (AI) and blockchain based on images captured by drones. Blockchain has immense potential to reduce the time and ensure the security of sharing forest fire data. The proposed system is constructed by combining Convolutional Neural Network (CNN) and transformers to support deep feature extraction with drone-based image capture and predictive modeling. This integrated system enables early detection of forest fires, accurate prediction, and effective response to mitigate the impact of wildfires. The proposed system is applied for the detection of burned areas caused by forest fires in northeastern Algeria. The results are highly promising and are based on spatial, temporal and fire features for effective forest fire prediction, enabling timely alerts and effective firefighting planning