Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions

IF 5.6 1区 工程技术 Q1 ENGINEERING, GEOLOGICAL
Yi Han, Shabnam J. Semnani
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引用次数: 0

Abstract

Rainfall-induced landslides have caused a large amount of economic losses and casualties over the years. Machine learning techniques have been widely applied in recent years to assess landslide susceptibility over regions of interest. However, a number of challenges limit the reliability and performance of machine learning-based landslide models. In particular, class imbalance in the dataset, selection of landslide conditioning factors, and potential extrapolation problems for landslide prediction under future conditions need to be carefully addressed. In this work, we introduce methodologies to address these challenges using XGBoost to train the landslide prediction model. Data resampling techniques are adopted to improve the model performance with the imbalanced dataset. Various models are trained and their performances are evaluated using a combination of different metrics. The results show that synthetic minority oversampling technique combined with the proposed gridded hyperspace sampling technique performs better than the other imbalance learning techniques with XGBoost. Subsequently, the extrapolation performance of the XGBoost model is evaluated, showing that the predictions remain valid for the projected climate conditions. As a case study, landslide susceptibility maps in California, USA are generated using the developed model and are compared with the historical California landslide catalog. These results suggest that the developed model can be of great significance in global landslide susceptibility mapping under climate change scenarios.

Abstract Image

未来气候条件下基于机器学习的滑坡易发性评估的重要考虑因素
多年来,降雨引发的山体滑坡造成了大量的经济损失和人员伤亡。近年来,机器学习技术已被广泛应用于评估相关区域的滑坡易发性。然而,一些挑战限制了基于机器学习的滑坡模型的可靠性和性能。特别是,数据集中的类不平衡、滑坡条件因子的选择以及在未来条件下预测滑坡的潜在外推法问题都需要认真解决。在这项工作中,我们介绍了利用 XGBoost 训练滑坡预测模型来应对这些挑战的方法。采用数据重采样技术来提高不平衡数据集的模型性能。对各种模型进行了训练,并使用不同的指标组合对其性能进行了评估。结果表明,合成少数过采样技术与所提出的网格超空间采样技术相结合,比其他不平衡学习技术和 XGBoost 性能更好。随后,对 XGBoost 模型的外推性能进行了评估,结果表明预测结果在预测的气候条件下仍然有效。作为案例研究,使用所开发的模型生成了美国加利福尼亚州的滑坡易发性地图,并与加利福尼亚州历史滑坡目录进行了比较。这些结果表明,所开发的模型在气候变化情景下绘制全球滑坡易发性地图方面具有重要意义。
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来源期刊
Acta Geotechnica
Acta Geotechnica ENGINEERING, GEOLOGICAL-
CiteScore
9.90
自引率
17.50%
发文量
297
审稿时长
4 months
期刊介绍: Acta Geotechnica is an international journal devoted to the publication and dissemination of basic and applied research in geoengineering – an interdisciplinary field dealing with geomaterials such as soils and rocks. Coverage emphasizes the interplay between geomechanical models and their engineering applications. The journal presents original research papers on fundamental concepts in geomechanics and their novel applications in geoengineering based on experimental, analytical and/or numerical approaches. The main purpose of the journal is to foster understanding of the fundamental mechanisms behind the phenomena and processes in geomaterials, from kilometer-scale problems as they occur in geoscience, and down to the nano-scale, with their potential impact on geoengineering. The journal strives to report and archive progress in the field in a timely manner, presenting research papers, review articles, short notes and letters to the editors.
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