期刊
GEOLOGICAL JOURNAL
卷 58, 期 6, 页码 2283-2301出版社
WILEY
DOI: 10.1002/gj.4666
关键词
deep learning; ensemble; landslide; machine learning; review; scarce data; transfer learning
Landslide susceptibility mapping (LSM) is crucial for development and construction planning to reduce the socio-economic impact of landslides. The use of machine learning algorithms and big data has greatly improved mapping accuracy and efficiency in this field.
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 to mitigating the social-eco impact associated with landslides. This paper comprehensively reviews the whole process of generating landslide susceptibility maps based on machine learning methods, introduces and compares the commonly used machine learning methods, and discusses the topics for future research.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据