Scholarlink

<p>The assessment of landslide susceptibility holds significant importance in disaster risk reduction. This study comprehensively examines the current research on landslide susceptibility from two aspects: the workflow of landslide susceptibility assessment and the associated modeling methods. Initially, we retrieved pertinent research articles, published between 2014 and 2023, and focused on “Landslide SensitivityAssessment” from the Web of Science database. Subsequently, we identified frequently occurring keywords in landslide susceptibility assessment studies employing ensemble learning methods during the past decade and created analytical charts. The standard methods for landslide inventory, evaluation indicators, and validation techniques were introduced along with their advantages and limitations. The shortcomings of eachmethod were identified, and potential future research directions were outlined. Finally, a detailed analysis of the use of ensemble methods in landslide susceptibility assessment was conducted; this is presented in several sections. The findings indicate that the advancement of ensemble learning methods has facilitated the development of landslide susceptibility assessment, rendering the landslide modeling process more efficient and accurate. In turn, this has enhanced the predictive capability of models in landslide susceptibility research. This review provides a systematic synthesis of ensemble learning methods in landslide susceptibility mapping, emphasizing their advantages, limitations, and application challenges. Rather than offering definitive solutions, it highlights critical gaps and outlines future directions to serve as a useful reference for ongoing research.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Ensemble learning for landslide susceptibility mapping: a review of machine learning and hybrid approaches

  • 期刊论文

  • 发表日期：

    2026年1月17日

查看全文
  • Hongwei Jiang,
  • Hao·Zhou,
  • Jiayi Wu,
  • Mengjie Liu,
  • Yuexu Wu,
  • Yongfan Guo

摘要

The assessment of landslide susceptibility holds significant importance in disaster risk reduction. This study comprehensively examines the current research on landslide susceptibility from two aspects: the workflow of landslide susceptibility assessment and the associated modeling methods. Initially, we retrieved pertinent research articles, published between 2014 and 2023, and focused on “Landslide SensitivityAssessment” from the Web of Science database. Subsequently, we identified frequently occurring keywords in landslide susceptibility assessment studies employing ensemble learning methods during the past decade and created analytical charts. The standard methods for landslide inventory, evaluation indicators, and validation techniques were introduced along with their advantages and limitations. The shortcomings of eachmethod were identified, and potential future research directions were outlined. Finally, a detailed analysis of the use of ensemble methods in landslide susceptibility assessment was conducted; this is presented in several sections. The findings indicate that the advancement of ensemble learning methods has facilitated the development of landslide susceptibility assessment, rendering the landslide modeling process more efficient and accurate. In turn, this has enhanced the predictive capability of models in landslide susceptibility research. This review provides a systematic synthesis of ensemble learning methods in landslide susceptibility mapping, emphasizing their advantages, limitations, and application challenges. Rather than offering definitive solutions, it highlights critical gaps and outlines future directions to serve as a useful reference for ongoing research.