A comprehensive review of machine learning‐based methods in landslide susceptibility mapping

作者：Songlin Liu, Luqi Wang, Wengang Zhang, Yuwei He, Pijush Samui · 发表于：Geological Journal · 年份：2023 · DOI：10.1002/gj.4666 · 被引用次数：196 · 研究领域：Landslides and related hazards、Flood Risk Assessment and Management、Geotechnical Engineering and Analysis

Landslide susceptibility mapping (LSM) has been widely used as an important reference for development and construction planning to mitigate the potential social‐eco impact caused by landslides. Originally, most of those maps were generated by the judgements of experts, which is time‐consuming and laborious, and whose accuracy is difficult to be quantified because of the subjective effects. With the development of machine learning algorithms and the methods of data collection, big data and artificial intelligence have now been popularized in this field, significantly improving mapping accuracy and efficiency. Various machine learning‐based methods, mainly including conventional machine learning, deep learning, and transfer learning have been applied and compared in LSM in different areas by previous researchers. Nevertheless, none of them can be effective in all cases. Although deep learning‐based methods were proven more accurate than conventional machine learning‐based methods in most data‐rich situations, the latter is sometimes more popularly used in LSM, as there is not that much data in this field to train a deep learning network perfectly. In a more rigorous situation when there is very limited data, transfer learning‐based approaches are applied by several researchers, which have contributed to improve the workability and the accuracy of LSM in data‐limited areas. Such technical explosion has promoted the application of landslide susceptibility maps, thus contributing ...