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

Hongwei Jiang, Hao Zhou, jiayi wu, Mengjie Liu, Yuexu Wu, Yongfan Guo

Abstract: 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 steps involved in landslide susceptibility assessment and 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 intelligence of models in landslide susceptibility research. The results of this study can help researchers understand the current conditions of landslide susceptibility research and provide a reference for subsequent research in this field.