Dynamic simulation and prediction technology for forest fire spread

Forest fires pose a serious threat to ecological security, and their frequency and intensity have increased under the combined influence of climate change and human activities. As a result, accurate fire risk prediction has become a top priority for forestry fire prevention and emergency management. However, in the complex environment of forest fires, existing research generally faces three major constraints: difficulty in identifying key variables due to multi-factor coupling, insufficient accuracy in long-term time series predictions, and a lack of verifiable physical basis for models, limiting the practical application of prediction results in fire prevention command systems.

To address these challenges, this paper proposes an analysis–prediction–verification trinity framework for forest fire prediction and situational assessment. This framework integrates data dimensionality reduction, time series modeling, and 3D visualization verification into a unified process, improving the accuracy, interpretability, and verifiability of prediction results, thereby meeting the transparency and reliability requirements of forest fire prevention actions. The framework structure is self-consistent and forms a closed loop, supporting a seamless transition from experimental analysis to field application, providing a practical path for the intelligent and digital transformation of forest fire prevention strategies. Specifically, the framework first utilizes principal component analysis (PCA) and Shapley additive explanations to clarify the relationships between various coupling factors and identify key influencing indicators. Secondly, an attention-based long short-term memory (ALSTM) network is constructed to improve the accuracy of long-term time series predictions and capture the inherent time lag effects in fire dynamics. Finally, a three-dimensional scenario model is built based on digital twin technology to achieve quantitative verification of prediction results and dynamic impact assessment of key factors.

This model combines high-precision time series prediction with an immersive virtual environment, opening up a new path for model verification centered on process traceability and scenario visibility. It breaks through the limitations of traditional black-box output and static evaluation, transforming the evolution of forest fires into a dynamic, visualized, and interactive process in three-dimensional space, helping emergency managers assess risks and take measurements at the right time when formulating preliminary plans and allocating resources.

The technological foundation established in this research has been demonstrated in a Sichuan Provincial science and technology project, Research and Demonstration Application of Multimodal Large Model and Intelligent Early Warning and Response Platform for Forest Fires. The platform has been demonstrated and applied, providing a reference model for the prediction and verification of complex systems in the public safety field, and promoting the transformation of management models from experience-driven to data- and mechanism-guided.