In this paper, a critical review of the landslide displacement prediction is conducted, based on a database of 359 articles on landslide displacement prediction published from 1985 to 2023. The statistical analysis of this database shows that the methods taken for the landslide displacement prediction could be categorized into physics-based and data-driven approaches. In the context of the physics-based approaches, the displacement of a landslide is characterized and predicted by a physics-based model that approximates the deformation mechanism of the landslide; whereas, the displacement, in the data-driven approaches, is often characterized and predicted by a mathematical or machine learning model, established based on analyses of the historical data. Note that although physics-based approaches were generally adopted in the early studies, data-driven approaches are becoming more and more popular in recent years. The main components involved in the physics-based approaches, including principles for establishing the prediction model, determination of model parameters, solution strategies of the model built, evaluation of the model's predictive performance, are first reviewed based on the literature database; then, those of the data-driven approaches, including methods for pre-processing the landslide displacement and influencing factors, algorithms for establishing the prediction model, calibration of model parameters, probabilistic prediction methods of landslide displacement, and evaluation of the model's predictive performance, are analyzed. Based on analyses of the information collected from the literature and our experience, we further discuss the challenges faced in landslide displacement prediction and offer recommendations for future research. We suggest that a hybrid prediction framework that takes advantage of both physics-based and data-driven approaches, a multi-field and multi-parameter landslide monitoring scheme, and an efficient strategy for the calibration of model parameters warrant further investigations.

中文翻译：

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斜坡尺度滑坡的位移预测：基于物理和数据驱动的方法综述


本文基于 1985 年至 2023 年出版的 359 篇滑坡位移预测文章数据库，对滑坡位移预测进行了批判性回顾。该数据库的统计分析表明，滑坡位移预测方法可分为基于物理的方法和数据驱动的方法。在基于物理的方法的背景下，滑坡的位移由近似于滑坡变形机制的基于物理的模型来表征和预测;而在数据驱动方法中，位移通常由数学或机器学习模型来表征和预测，该模型基于历史数据分析建立。请注意，尽管早期研究通常采用基于物理的方法，但近年来数据驱动的方法越来越受欢迎。首先，基于文献数据库综述了基于物理的方法所涉及的主要组成部分，包括建立预测模型的原则、模型参数的确定、所构建模型的求解策略、模型预测性能的评估;然后，分析了数据驱动的方法，包括滑坡位移及其影响因素的预处理方法、建立预测模型的算法、模型参数的校准、滑坡位移的概率预测方法以及模型预测性能的评价。基于对文献中收集的信息的分析和我们的经验，我们进一步讨论了滑坡位移预测面临的挑战，并为未来的研究提供了建议。 我们建议，一个利用基于物理和数据驱动方法的混合预测框架、多领域和多参数的滑坡监测方案以及模型参数校准的有效策略值得进一步研究。