Forest fire risk prediction is essential for building a forest fire defense system. Ensemble learning methods can avoid the problem of difficult model selection for disaster susceptibility prediction and can significantly improve modeling accuracy. This study introduces a stacking ensemble learning model for predicting forest fire risks in Yunnan Province by integrating various data types, such as meteorological, topographic, vegetation, and human activity factors. A total of 70,274 fire points and an equal number of randomly selected nonfire points were used to develop the model, with 70% of the data allocated for training and the remaining 30% for testing. The stacking model combined four diverse machine learning methods: random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP). We evaluated the model’s predictive performance using metrics like accuracy, area under the characteristic curve (AUC), and fire density (FD). The results demonstrated that the stacking fusion model exhibited remarkable accuracy with an AUC of 0.970 on the test set, significantly surpassing the performance of individual machine learning models, which had AUC values ranging from 0.935 to 0.953. Furthermore, the stacking fusion model effectively captured the maximum fire density in extremely high susceptibility areas, demonstrating enhanced generalization capabilities.

As thermoplastic materials are widely used in buildings, the fire hazards of thermoplastic materials are increasingly becoming a central issue in fire safety research due to their unique pyrolysis and melting mechanisms. In this paper, the features and common types of thermoplastic materials are introduced first. Then, the combustion behavior of thermoplastic materials is theoretically analyzed based on the empirical formulas and heat balance equations, such as the pyrolysis kinetics, ignition time, melting and dripping, flame, burning rate and mass loss rate, temperature and heat flow, gas products, and influencing factors. The influencing factors basically include the sample properties (width, incline angle, and thickness, etc.), the façade structure (sidewalls, curtain wall, etc.), the ambient conditions (altitude, pressure, and gravity, etc.), and the flame retardant treatment. Similarly, this study also illustrates the vertical and horizontal flame spread behavior of the thermoplastic materials and the influencing factors. The utilized methods include the experimental methods, the analytical methodologies, and the approaches for numerical simulation. Finally, the problems encountered at this stage and worthy of further study in the future are presented.