Advancements in Technologies and Methodologies of Machine Learning in Landslide Susceptibility Research: Current Trends and Future Directions

被引:0
|
作者
Lu, Zongyue [1 ,2 ]
Liu, Genyuan [1 ,2 ]
Song, Zhihong [1 ,2 ]
Sun, Kang [1 ,2 ]
Li, Ming [1 ,2 ]
Chen, Yansi [1 ,2 ]
Zhao, Xidong [3 ,4 ]
Zhang, Wei [1 ,2 ]
机构
[1] China Geol Survey, Ctr Geophys Survey, Langfang 065000, Peoples R China
[2] China Geol Survey, Technol Innovat Ctr Earth Near Surface Detect, Langfang 065000, Peoples R China
[3] China Geol Survey, Harbin Ctr Integrated Nat Resources Survey, Harbin 150086, Peoples R China
[4] Minist Nat Resources, Observat & Res Stn Earth Crit Zone Black Soil, Harbin 150086, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 21期
关键词
machine learning; landslide; susceptibility assessment; model interpretability; spatial heterogeneity; SUPPORT VECTOR MACHINE; ARTIFICIAL NEURAL-NETWORK; LOGISTIC-REGRESSION; FREQUENCY RATIO; MODELS; SVM;
D O I
10.3390/app14219639
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Landslides are pervasive geological hazards that pose significant risks to human life, property, and the environment. Understanding landslide susceptibility is crucial for predicting and mitigating these disasters. This article advocates for a comprehensive review by systematically compiling and analyzing 146 relevant studies up to 2024. It assesses current progress and limitations and offers guidance for future research. This paper provides a comprehensive overview of the diverse challenges encountered by machine learning models in landslide susceptibility assessment, encompassing aspects such as model selection, the formulation of evaluation index systems, model interpretability, and spatial heterogeneity. The construction of an evaluation index system, which serves as the foundational data for the model, profoundly influences its accuracy. This study extensively investigates the selection of evaluation factors and the identification of positive and negative samples, proposing valuable methodologies. Furthermore, this paper briefly deliberates and compares classical machine learning models, offering valuable insights for model selection. Additionally, it delves into discussions concerning model interpretability and spatial heterogeneity issues. These research findings promise to enhance the precision of landslide susceptibility assessments and furnish effective strategies for risk management.
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收藏
页数:29
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