A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
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Autoencoders can be used as a preprocessing step before a classical classifier or as components within a deep CNN.
“Since they can remove noise and simplify data, AEs can be used as a preprocessing step before a classical classifier (such as a SVM) [75] or even as components within a deep CNN [76,77].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [77]
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · see citing works
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Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“Since they can remove noise and simplify data, AEs can be used as a preprocessing step before a classical classifier (such as a SVM) [75] or even as components within a deep CNN [76,77].”
Method
- Authors
- Huang, F., Zhang, J., Zhou, C., Wang, Y., Huang, J., & Zhu, L.
- Affiliations
- Nanchang University (CN)
- University of Newcastle Australia (AU)
- Corresponding author
- Zhu, Li
- Venue
- Landslides
- Published
- 2019-09-11
- Volume
- 17
- Issue
- 1
- Pages
- 217-229
- ISSN
- 1612-510X (ISSN-L)
- 1612-5118
- OpenAlex
- W2972534151
- Cited by
- 444
- References
- 1
- Citations by year
- 2026: 34 · 2025: 48 · 2024: 88 · 2023: 120 · 2022: 65 · 2021: 50 · 2020: 35 · 2019: 3
- FWCI
- 49.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Landslides (publishedVersion)
- NOVA (University of Newcastle Australia) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Landslides and related hazards
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Geotechnical Engineering and Analysis
- Physical Sciences › Engineering › Mechanical Engineering › Tree Root and Stability Studies
- Keywords
- landslide susceptibility prediction, sparse autoencoders, fully connected neural network, environmental factors, feature extraction, frequency ratio, unsupervised feature learning, nonlinear feature extraction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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