XGBoost在滑坡预测中的应用研究

Research on the Application of XGBoost in Landslide Prediction

摘要: 滑坡是一种强破坏力的自然灾害，严重威胁人类生命安全与社会经济，传统的预测方法在面对复杂环境存在明显的局限性，严重影响预测效果和应用场景，随着多维数据的兴起，能处理大量数据的机器学习模型成为主流，XGBoost以其独特的优势，在滑坡预测中表现出强大的预测精度，且在样本不平衡问题具有较强鲁棒性。本文结合了XGBoost在区域易发性评价、滑坡位移预测以及XGBoost多模型集成在阶跃型滑坡位移上的应用，进一步展现了XGBoost在不同类型的滑坡预测中强大的适应性。应用前景上，XGBoost在未来可以与深度学习、集成代价敏感学习与不确定性量化方法等多方位融合，使滑坡预测能够更加精准、可靠、实用，构建更加智能化、准确化的滑坡风险管理系统，为滑坡风险评估和防灾决策提供支持。

Abstract: Landslides are a highly destructive natural disaster that poses a serious threat to human life safety and socio-economic development. Traditional prediction methods have obvious limitations in dealing with complex environments, seriously affecting prediction effectiveness and application scenarios. With the rise of multidimensional data, machine learning models capable of processing large amounts of data have become mainstream. XGBoost, with its unique advantages, exhibits strong prediction accuracy in landslide prediction and strong robustness in sample imbalance problems. This article combines XGBoost in regional susceptibility assessment, landslide displacement prediction, and the application of XGBoost multi model integration in step landslide displacement, further demonstrating the strong adaptability of XGBoost in predicting different types of landslides. In terms of application prospects, XGBoost can be integrated with deep learning, integrated cost sensitive learning, and uncertainty quantification methods in multiple aspects in the future, making landslide prediction more accurate, reliable, and practical, and building a more intelligent and accurate landslide risk management system to provide support for landslide risk assessment and disaster prevention decision-making.