2021
DOI: 10.1016/j.enggeo.2021.106103
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AI-powered landslide susceptibility assessment in Hong Kong

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Cited by 134 publications
(26 citation statements)
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“…Thus, Bidirectional-LSTM was employed to learn the factor data of forward and backward information. This Bi-LSTM aimed to model the inter-relationship among the factors from sequential observed data [ 44 , 45 ].…”
Section: Methodsmentioning
confidence: 99%
“…Thus, Bidirectional-LSTM was employed to learn the factor data of forward and backward information. This Bi-LSTM aimed to model the inter-relationship among the factors from sequential observed data [ 44 , 45 ].…”
Section: Methodsmentioning
confidence: 99%
“…Landslide susceptibility mapping methods have been reviewed comprehensively by some scholars [5,54,67] and thus are not covered in this article. Recent studies have shown that methods based on CNN also perform the best in landslide susceptibility mapping [31,[68][69][70]. If the CNN-predicted landslide susceptibility can be used as prior knowledge in the detection process, it is promising to further improve the detection perforce of FCNN.…”
Section: ) Landslide Detection Under the Guidance Of Susceptibilitymentioning
confidence: 99%
“…And the other one, corresponding to the field of machine learning, where performance maximization is sought instead, at the expense of interpretability (i.e., prediction task). Two common examples respectively correspond to statistical models such as Generalized Linear Models (e.g., Castro Camilo et al, 2017) and to machine learning models such as decision trees (e.g., Yeon et al, 2010) or neural networks (e.g., Wang et al, 2021). In between these two lies the Generalized Additive Model, also referred to as an interpretable machine learning technique (Goetz et al, 2011;Steger et al, 2021).…”
Section: Introductionmentioning
confidence: 99%