Estimation of Wildfire Conditions via Perimeter and Surface Area Optimization Using Convolutional Neural Network

Wildfires are a major natural disaster that can cause significant damage to ecosystems and human communities. Wildfire behavior must be predicted accurately for effective emergency response and evacuation planning. The proposed system suggests a novel Convolutional Neural Networks (CNNs) method to estimate wildfire conditions via optimization of perimeter and surface area. Extraction of the required features is from the historical wildfire data which is performed before preprocessing and training. The trained CNN is then validated and optimized for performance, with the goal of accurately predicting wildfire behavior in real-time. The proposed system results show the effectiveness of the method, improving the wildfire prediction ability. The outcome of the prediction determines the probability of a wildfire. The results can be used to monitor areas where wildfires are expected. This helps to implement strategies that can be used to mitigate the effects of wildfires. The combination of perimeter and surface area optimization with CNNs represents a promising new approach to wildfire prediction and management, with broad applications in land management, sustainability, and emergency response.