Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, China Article Swipe
Weida Ni
,
Liuyuan Zhao
,
Lele Zhang
,
Ke Xing
,
Jie Dou
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15092296
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/rs15092296
Disasters caused by landslides pose a considerable threat to people’s lives and property, resulting in substantial losses each year. Landslide displacement rate prediction (LDRP) provides a useful fundamental tool for mitigating landslide disasters. However, more accurately predicting LDRP remains a challenge in the study of landslides. Lately, ensemble deep learning algorithms have shown promise in delivering a more precise and effective spatial modeling solution. The core aims of this research are to explore and evaluate the prediction capability of three progressive evolutionary deep learning (DL) techniques, i.e., a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU) ensemble AdaBoost algorithm for modeling rainfall-induced and reservoir-induced landslides in the Baihetan reservoir area in China. The outcomes show that the ensemble DL model could predict the Wangjiashan landslide in the Baihetan reservoir area with improved accuracy. The highest accuracy was achieved in the testing set when the window length equaled 30. However, assembling two predictors outperformed the accuracy of assembling three predictors, with the mean absolute error and root mean square error reaching 1.019 and 1.300, respectively. These findings suggest that the combination of strong learners and DL can yield satisfactory prediction results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs15092296
- https://www.mdpi.com/2072-4292/15/9/2296/pdf?version=1682578041
- OA Status
- gold
- Cited By
- 15
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367182014
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367182014Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs15092296Digital Object Identifier
- Title
-
Coupling Progressive Deep Learning with the AdaBoost Framework for Landslide Displacement Rate Prediction in the Baihetan Dam Reservoir, ChinaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-27Full publication date if available
- Authors
-
Weida Ni, Liuyuan Zhao, Lele Zhang, Ke Xing, Jie DouList of authors in order
- Landing page
-
https://doi.org/10.3390/rs15092296Publisher landing page
- PDF URL
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https://www.mdpi.com/2072-4292/15/9/2296/pdf?version=1682578041Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/15/9/2296/pdf?version=1682578041Direct OA link when available
- Concepts
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Landslide, AdaBoost, Ensemble learning, Random forest, Computer science, Displacement (psychology), Artificial intelligence, Geology, Seismology, Support vector machine, Psychology, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 9, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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