Fire is among the most dangerous and devastating natural hazards in forest ecosystems around the world. The development of computational ensemble models for improving the predictive accuracy of forest fire susceptibilities could save time and cost in firefighting efforts. Here, we combined a locally weighted learning (LWL) algorithm with the Cascade Generalization (CG), Bagging, Decorate, and Dagging ensemble learning techniques for the prediction of forest fire susceptibility in the Pu Mat National Park, Nghe An Province, Vietnam. A geospatial database that contained records from 56 historical fires and nine explanatory variables was employed to train the standalone LWL model and its derived ensemble models. The models were validated for their goodness-of-fit and predictive capability using the area under the receiver operating characteristic curve (AUC) and several other statistical performance criteria. The CG-LWL and Bagging-LWL models with AUC = 0.993 showed the highest training performance, whereas the Dagging-LWL ensemble model with AUC = 0.983 performed better than Decorate-LWL (AUC = 0.976), CG-LWL and Bagging-LWL (AUC = 0.972), and LWL (AUC = 0.965) for predicting the spatial pattern of fire susceptibilities across the study area. Our study promotes the application of ensemble models in forest fire prediction and enhances the researchers' understanding of the processes of model building. Although these four ensemble models were originally developed for the estimation of forest fire susceptibility, the models are sufficiently general to be used for predicting other types of natural hazards, such as landslides, floods, and dust storms, by considering local geo-environmental factors.

火灾是全世界森林生态系统中最危险，最破坏性的自然灾害之一。开发用于提高森林火灾敏感性的预测准确性的计算集成模型可以节省消防工作的时间和成本。在这里，我们将局部加权学习（LWL）算法与级联（CG），装袋，装饰和Dagging集成学习技术相结合，以预测越南Nghe An省Pu Mat国家公园的森林火灾敏感性。利用包含来自56次历史大火的记录和9个解释变量的地理空间数据库来训练独立的LWL模型及其派生的集成模型。使用接收器工作特性曲线（AUC）下的面积和其他几个统计性能标准，对模型的拟合优度和预测能力进行了验证。AUC = 0.993的CG-LWL和Bagging-LWL模型表现出最高的训练性能，而AUC = 0.983的Dagging-LWL集成模型表现优于Decorate-LWL（AUC = 0.976），CG-LWL和Bagging-LWL（ AUC = 0.972）和LWL（AUC = 0.965）用于预测整个研究区域火灾敏感性的空间格局。我们的研究促进了集成模型在森林火灾预测中的应用，并增强了研究人员对模型构建过程的理解。尽管这四个集成模型最初是为估算森林火灾的敏感性而开发的，