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Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to improving the spatiotemporal targeting of fire prevention efforts. In this study, Nanning, China, was selected as the research area. The wildfire driving factors were chosen from both seasonal and nonseasonal aspects, and the datasets were divided into five periods: all seasons, spring, summer, autumn, and winter. The light gradient boosting machine (LGBM) was employed to construct wildfire danger models for different periods, evaluating the spatial distribution of high-wildfire-danger areas during these periods and the predictive performance differences. The SHapley Additive exPlanations (SHAP) method was utilized to analyze the differential contributions of various factors to wildfire occurrence in different seasons. Subsequently, the remote sensing ecological index (RSEI) was calculated using four indicators, greenness, heat, wetness, and dryness, to assess the ecological vulnerability in different seasons. Finally, by integrating danger and vulnerability information, wildfire risk models were developed to systematically assess the risk of wildfire disasters causing losses to the ecological environment in different seasons. The results indicate that: (1) The evaluation of wildfire danger based on individual seasons effectively compensates for the shortcomings of analyzing danger across all seasons, exhibiting higher predictive performance and richer details. (2) Wildfires in Nanning primarily occur in spring and winter, while the likelihood of wildfires in summer and autumn is relatively lower. In different seasons, NDVI is the most critical factor influencing wildfire occurrence, while slope is the most important nonseasonal factor. The influence of factors varies among different seasons, with seasonal factors having a more significant impact on wildfire danger. (3) The ecological vulnerability in Nanning exhibits significant differences between different seasons. Compared to spring and winter, the ecological environment is more vulnerable to wildfire disasters during summer and autumn. (4) The highest wildfire risk occurs in spring, posing the greatest threat to the ecological environment, while the lowest wildfire risk is observed in winter. Taking into account information on danger and vulnerability in different seasons enables a more comprehensive assessment of the risk differences in wildfire disasters causing ecological losses. The research findings provide a scientific theoretical basis for relevant departments regarding the prevention, control, and management of seasonal wildfires. [ABSTRACT FROM AUTHOR]

The frequent occurrence and spread of wildfires pose a serious threat to the ecological environment and urban development. Therefore, assessing regional wildfire susceptibility is crucial for the early prevention of wildfires and formulation of disaster management decisions. However, current research on wildfire susceptibility primarily focuses on improving the accuracy of models, while lacking in-depth study of the causes and mechanisms of wildfires, as well as the impact and losses they cause to the ecological environment and urban development. This situation not only increases the uncertainty of model predictions but also greatly reduces the specificity and practical significance of the models. We propose a comprehensive evaluation framework to analyze the spatial distribution of wildfire susceptibility and the effects of influencing factors, while assessing the risks of wildfire damage to the local ecological environment and urban development. In this study, we used wildfire information from the period 2013–2022 and data from 17 susceptibility factors in the city of Guilin as the basis, and utilized eight machine learning algorithms, namely logistic regression (LR), artificial neural network (ANN), K-nearest neighbor (KNN), support vector regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM), and eXtreme gradient boosting (XGBoost), to assess wildfire susceptibility. By evaluating multiple indicators, we obtained the optimal model and used the Shapley Additive Explanations (SHAP) method to explain the effects of the factors and the decision-making mechanism of the model. In addition, we collected and calculated corresponding indicators, with the Remote Sensing Ecological Index (RSEI) representing ecological vulnerability and the Night-Time Lights Index (NTLI) representing urban development vulnerability. The coupling results of the two represent the comprehensive vulnerability of the ecology and city. Finally, by integrating wildfire susceptibility and vulnerability information, we assessed the risk of wildfire disasters in Guilin to reveal the overall distribution characteristics of wildfire disaster risk in Guilin. The results show that the AUC values of the eight models range from 0.809 to 0.927, with accuracy values ranging from 0.735 to 0.863 and RMSE values ranging from 0.327 to 0.423. Taking into account all the performance indicators, the XGBoost model provides the best results, with AUC, accuracy, and RMSE values of 0.927, 0.863, and 0.327, respectively. This indicates that the XGBoost model has the best predictive performance. The high-susceptibility areas are located in the central, northeast, south, and southwest regions of the study area. The factors of temperature, soil type, land use, distance to roads, and slope have the most significant impact on wildfire susceptibility. Based on the results of the ecological vulnerability and urban development vulnerability assessments, potential wildfire risk areas can be identified and assessed comprehensively and reasonably. The research results of this article not only can improve the specificity and practical significance of wildfire prediction models but also provide important reference for the prevention and response of wildfires. [ABSTRACT FROM AUTHOR]