A Forest Fire Prediction Framework Based on Multiple Machine Learning Models

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Fire risk prediction is of great importance for fire prevention. Fire risk maps are an effective tool to quantify regional fire risk. Most existing studies on forest fire risk maps mainly use a single machine learning model, but different models have varying degrees of feature extraction in the same spatial environment, leading to inconsistencies in prediction accuracy. To address this issue, this study proposes a novel integrated machine learning framework that systematically evaluates multiple models and combines their outputs through a weighted ensemble approach, thereby enhancing prediction robustness. During the feature selection stage, factors including socio-economic, climate, terrain, remote sensing data, and human factors were considered. Unlike previous studies that mainly use a single model, eight models were evaluated and compared using performance metrics. Three models were weighted based on Mean Squared Error (MSE) values, and cross-validation results showed an improvement in model performance. The integrated model achieved an accuracy of 0.8602, an area under the curve (AUC) of 0.772, and superior sensitivity (0.9234), outperforming individual models. Finally, the weighted framework was applied to generate a fire risk map. Compared with prior studies, this multi-model ensemble approach not only improves predictive accuracy but also provides a scalable and adaptable framework for fire risk mapping, and provides valuable insights to address future fire sustainability issues.