Application and prospects of machine learning for rockfalls, landslides and debris flows
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摘要:
崩塌、滑坡、泥石流是常见的山区斜坡地质灾害，对人民生命财产安全构成严重威胁。计算机技术的迅速发展和“大数据”时代的到来，为崩塌、滑坡和泥石流灾害风险防控提供了新动力，人工智能成为备受关注的前沿研究内容，而机器学习算法是其中应用最为广泛的方法之一。在大量文献调研的基础上，从经典机器学习算法和深度学习算法2个方面综述了机器学习算法在崩滑流灾害领域的应用，并对目前存在的问题和未来发展方向进行阐述。总结认为：（1）经典机器学习算法可分为监督学习、无监督学习和强化学习3种，广泛应用在滑坡和泥石流灾害的易发性评价中，普遍认为随机森林模型具有较高的预测精度和建模适应能力；（2）常用的深度学习架构包括自编码器、深度置信网络、卷积神经网络和循环神经网络4类，主要应用于崩滑流灾害的识别、易发性评价及位移预测；（3）未来研究需重点加强数据质量与数量、提升模型的可解释性、增强模型可靠性与泛化性、构建实时监测预警系统，推动实现地质灾害的自动识别和快速响应。研究结果为采用机器学习开展斜坡地质灾害防灾减灾工作提供支撑和研究方向。
Abstract:Rockfalls, landslides, and debris flows present significant threats to the safety of mountainous communities globally. With the rapid development of computer technology and the onset of the “big data” era, new avenues and momentum have emerged in disaster prevention and mitigation. Artificial intelligence, notably machine learning algorithms, has emerged as a hot point in this domain. Drawing upon an extensive literature review, this paper provides an overview of the application of machine learning algorithms, encompassing classical and deep learning methodologies. The current issues and future development directions are also discussed. This study highlights the critical role of classical machine learning algorithms—such as supervised, unsupervised, and reinforcement learning in assessing the susceptibility to landslides and debris flow hazards. Notably, the random forest model stands out for its high predictive accuracy and versatile modeling adaptability, making it a dependable tool for landslide susceptibility prediction. Deep learning architectures, including autoencoders, deep belief networks, convolutional neural networks, and recurrent neural networks, are instrumental in hazard identification, susceptibility assessment, and displacement prediction. Future research should prioritize enhancing data quality and quantity, optimizing model interpretability, improving model reliability and generalization, and establishing real-time monitoring and warning systems for automatic identification and rapid response to geological hazards. This study provides support and research directions for the prevention and mitigation of slope geological hazards using machine learning techniques.
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表 1 传统机器学习技术在滑坡识别上的应用效果与应用范围
Table 1. The application effect and scope of classic machine learning technology
方法 识别效果 优点 缺点 应用范围 最大似然法（MLE） 较差 简单，易实现 训练样本必须超过波段数 波段较少的数据 支持向量机（SVM） 小数据集较好，
大数据集较差泛化能力比较强，无须进行
数据降维，易解释对核函数以及参数敏感，
大样本时，效率并不是很高小样本、非线性
识别问题随机森林（RF） 小数据集较差，
大数据集较好泛化能力强，训练速度快，不用做
特征选择，具有稳定的可移植性解释性差 大规模数据和
高维特征方面
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