Shamima Aktar

Forest and grassland fire behavior prediction is increasingly critical under climate change, as rising fire frequency and intensity threaten ecosystems and human societies worldwide. This paper reviews the status and future development trends of wildfire behavior modeling and prediction technologies. It provides a comprehensive overview of the evolution of models from empirical to physical and then to data-driven approaches, emphasizing the integration of multidisciplinary techniques such as machine learning and deep learning. While conventional physical models offer mechanistic insights, recent advancements in data-driven models have enabled the analysis of big data to uncover intricate nonlinear relationships. We underscore the necessity of integrating multiple models via complementary, weighted fusion and hybrid methods to bolster robustness across diverse situations. Ultimately, we advocate for the creation of intelligent forecast systems that leverage data from space, air and ground sources to provide multifaceted fire behavior predictions in regions and globally. Such systems would more effectively transform fire management from a reactive approach to a proactive strategy, thereby safeguarding global forest carbon sinks and promoting sustainable development in the years to come. By offering forward-looking insights and highlighting the importance of multidisciplinary approaches, this review serves as a valuable resource for researchers, practitioners, and policymakers, supporting informed decision-making and fostering interdisciplinary collaboration.

Control of forest fire ignition sources is the top priority in fire management practices. China has gained great success in reducing forest fires in recent years, and the relevant safety measures taken during this process are worthy of investigation and publicity. Based on fire statistical data through the years between 2003 and 2017, we analyzed the detailed classification of fire ignition sources and their contribution to the annual forest fire occurrence. The role of different ignition sources in altering fire occurrence was quantified and ranked by defining a contribution extent parameter. A statistical tool was also applied to conduct correlation analysis to identify variation patterns of time series data from individual fire causes. The annual fire numbers declined after 2008 and stabilized at a level < 2000 in recent years, pointing to the containment of several major ignition sources. Starting from the legislative development, an accountability system was established at all levels from administrative heads to local residents, paving the way for the multifaceted and full-coverage fire prevention publicity and education as well as the fire use restriction in particular seasons. The effectiveness of management measures in lessening forest fire occurrence was interpreted using the results of correlation analysis among the fire numbers initiated by individual ignition sources.