ISSN 1000-3665 CN 11-2202/P

    机器学习在斜坡地质灾害领域的应用现状与展望

    Application and prospects of machine learning for rockfalls, landslides and debris flows

    • 摘要: 崩塌、滑坡与泥石流作为最普遍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、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。文章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两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了阐述,结果表明:(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,主要应用在崩滑流灾害的识别、易发性评价及位移预测中;(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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