Construction and Application of Extreme Event Probability Models Based on Machine Learning

The increasing frequency of extreme events poses a significant threat to engineering safety and socio-economic stability. Traditional prediction and assessment models exhibit notable limitations when dealing with complex and dynamic environments, whereas machine learning methods, leveraging their powerful data processing capabilities and adaptability, demonstrate broad application prospects. However, existing research still lacks comprehensive solutions when applying machine learning to complex engineering systems, particularly in the risk assessment and prediction of extreme events. In light of this, this study focuses on flood risk assessment and constructs an integrated prediction model based on multiple machine learning algorithms. Through correlation analysis, cluster analysis, ensemble learning, and neural network enhancement, this study identifies key influencing factors of flood occurrence and establishes flood risk evaluation and probability prediction models. Shapley Additive exPlanations (SHAP) value analysis reveals that policy factors and coastal vulnerability contribute most significantly to the model output, with SHAP importance values exceeding 35 and approaching 30, respectively, indicating their critical impact on flood risk prediction. Furthermore, the CatBoost-based early warning evaluation model achieves an accuracy of 0.9346 and an F1 score of 0.9246 on the test set, while the random forest model achieves an MSE of 0.000153 and an MAE of 0.00172. Additionally, the neural network-enhanced random forest model maintains high prediction accuracy even when using only five key indicators. These results demonstrate the significant advantages of machine learning methods in flood risk assessment and prediction, providing reliable support for engineering safety and disaster prevention and mitigation.