Advancements in Forest Fire Prediction: Techniques and Technologies

Life-threatening effects, including human casualties, environmental damage, and significant financial damages, are caused by forest fires, which continue to be an issue on a worldwide scale. Predicting and detecting forest fires promptly is crucial for mitigating these risks. More and more people are interested in using state-of-the-art methods for forest fire control as a reaction to this issue and because of the present scenario of humans living in the digital age of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), the Internet of Things (IoT), and Wireless Sensor Networks (WSN). In this work, a wide range of wildfire prediction models like AI-based, ML-based, DL-based, IoT-based, WSN-based, Remote sensing & Satellite imagery, and Unmanned Aerial Vehicles (UAV)-based, Edge computing-based, Weather-based, Ground-based sensor networks were reviewed. This work also provided the latest techniques and models that can be employed in the future for predicting wildfires. Finally, the study offers recommendations for future research that make use of cutting-edge technologies such as digital twins, edge computing, Liquid Neural Networks (LNN), and eXplainable Artificial Intelligence (XAI), and assesses the possible influence of wildfire prediction models on wildfire management. The study concludes by discussing the current state of wildfire prediction models and their limitations in wildfire management, and by recommending future studies to address these issues so that these models may have the greatest possible positive influence.