Abstract Landslides are one of the catastrophic natural hazards that occur in mountainous areas, leading to loss of life, damage to properties, and economic disruption. Landslide susceptibility models prepared in a Geographic Information System (GIS) integrated environment can be key for formulating disaster prevention measures and mitigating future risk. The accuracy and precision of susceptibility models is evolving rapidly from opinion-driven models and statistical learning toward increased use of machine learning techniques. Critical reviews on opinion-driven models and statistical learning in landslide susceptibility mapping have been published, but an overview of current machine learning models for landslide susceptibility studies, including background information on their operation, implementation, and performance is currently lacking. Here, we present an overview of the most popular machine learning techniques available for landslide susceptibility studies. We find that only a handful of researchers use machine learning techniques in landslide susceptibility mapping studies. Therefore, we present the architecture of various Machine Learning (ML) algorithms in plain language, so as to be understandable to a broad range of geoscientists. Furthermore, a comprehensive study comparing the performance of various ML algorithms is absent from the current literature, making an assessment of comparative performance and predictive capabilities difficult. We therefore undertake an extensive analysis and comparison between different ML techniques using a case study from Algeria. We summarize and discuss the algorithm's accuracies, advantages and limitations using a range of evaluation criteria. We note that tree-based ensemble algorithms achieve excellent results compared to other machine learning algorithms and that the Random Forest algorithm offers robust performance for accurate landslide susceptibility mapping with only a small number of adjustments required before training the model.

中文翻译：

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滑坡敏感性研究的机器学习方法：算法性能的比较概述

摘要 山体滑坡是发生在山区的灾难性自然灾害之一，会导致人员伤亡、财产损失和经济中断。在地理信息系统 (GIS) 集成环境中准备的滑坡敏感性模型可能是制定防灾措施和减轻未来风险的关键。敏感性模型的准确性和精确度正在从意见驱动模型和统计学习迅速发展为机器学习技术的更多使用。对滑坡敏感性绘图中意见驱动模型和统计学习的批判性评论已经发表，但目前缺乏对当前滑坡敏感性研究机器学习模型的概述，包括其操作、实施和性能的背景信息。这里，我们概述了可用于滑坡敏感性研究的最流行的机器学习技术。我们发现只有少数研究人员在滑坡敏感性绘图研究中使用机器学习技术。因此，我们以通俗易懂的语言展示了各种机器学习 (ML) 算法的架构，以便广大地球科学家能够理解。此外，当前文献中缺乏比较各种 ML 算法性能的综合研究，这使得比较性能和预测能力的评估变得困难。因此，我们使用阿尔及利亚的案例研究对不同的机器学习技术进行了广泛的分析和比较。我们总结并讨论了算法的准确性，使用一系列评估标准的优点和局限性。我们注意到，与其他机器学习算法相比，基于树的集成算法取得了出色的结果，并且随机森林算法为准确的滑坡敏感性映射提供了强大的性能，只需在训练模型之前进行少量调整即可。