HARNESSING DEEP LEARNING FOR WILDFIRE RISKS PREDICTION: A NOVEL APPROACH

Abstract

This article presents a pioneering approach for predicting wildfires risks using deep learning techniques. By combining convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Adaptive Moment Estimation (ADAM), our framework analyses geospatial and environmental data to capture the intricate dynamics of disasters. Our model integrates satellite imagery, climate data, socioeconomic factors, and historical records to accurately assess risks. Leveraging transfer learning, we optimize training efficiency with pre-trained models. Extensive experiments demonstrate the superior performance of our deep learning framework compared to traditional methods. With its ability to enable proactive planning and decision-making, our approach strengthens disaster preparedness and response strategies. This research represents a significant advancement in utilizing deep learning for predicting wildfires risks, paving the way for further innovations in this vital field.