Deep-Learning based Forest Fire Prediction Models: A Review with Key Insights and Open Challenges

Abstract

Forest fires have become one of the most destructive environmental hazards, causing severe ecological, economic, and human losses worldwide. Exact and well-timed forest fire forecasting plays a crucial role in reducing destruction and improving disaster response systems. This forest fire prediction paper presents a meaningful analysis of recent works developed using Machine Learning (ML), Deep Learning (DL), ensemble learning, optimization techniques, and computer vision approaches. The analysis reveals that DL and hybrid intelligent systems significantly improve prediction accuracy and real-time detection capability compared to conventional statistical methods. However, major challenges persist, including limited real-time data integration, weak generalization across diverse environments, high computational complexity, poor smoke-level detection, data imbalance, overfitting, and insufficient multisource data fusion. This paper further identifies critical research gaps and discusses sustainable solutions such as multimodal data fusion, IoT-assisted monitoring, uncertainty-aware prediction systems, and lightweight edge-based artificial intelligence frameworks.