Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network

Collapse susceptibility evaluation based on an improved two-step sampling strategy and a convolutional neural network

Objective Machine learning has been widely applied in the fields of collapse, landslide and debris flow susceptibility analysis. The selection of nonhazard samples is a key issue in landslide susceptibility analysis. Traditional random sampling and manual labelling methods may involve randomness and...

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Bibliographic Details
Main Authors: Rilang DENG, Qinghua ZHANG, Wei LIU, Lingwei CHEN, Jianhui TAN, Zemao GAO, Xianchang ZHENG
Format: Article
Language:zho
Published: Editorial Department of Bulletin of Geological Science and Technology 2024-03-01
Series:地质科技通报
Subjects:
collapse
susceptibility evaluation
positive and unlabeled (pu) learning
spy technique
information value
convolutional neural network
random forest
support vector machine
Online Access:https://dzkjqb.cug.edu.cn/en/article/doi/10.19509/j.cnki.dzkq.tb20220535

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