Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping Article Swipe
Sunil Saha
,
Jagabandhu Roy
,
Tusar Kanti Hembram
,
Biswajeet Pradhan
,
Abhirup Dikshit
,
Khairul Nizam Abdul Maulud
,
Abdullah Alamri
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/w13192664
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/w13192664
The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslides conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/w13192664
- https://www.mdpi.com/2073-4441/13/19/2664/pdf?version=1632823453
- OA Status
- gold
- Cited By
- 41
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3204564844
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3204564844Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/w13192664Digital Object Identifier
- Title
-
Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility MappingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-09-27Full publication date if available
- Authors
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Sunil Saha, Jagabandhu Roy, Tusar Kanti Hembram, Biswajeet Pradhan, Abhirup Dikshit, Khairul Nizam Abdul Maulud, Abdullah AlamriList of authors in order
- Landing page
-
https://doi.org/10.3390/w13192664Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-4441/13/19/2664/pdf?version=1632823453Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4441/13/19/2664/pdf?version=1632823453Direct OA link when available
- Concepts
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Landslide, Artificial intelligence, Decision tree, Deep learning, Computer science, Artificial neural network, Machine learning, Tree (set theory), Decision tree model, Logistic regression, Data mining, Geology, Mathematics, Geotechnical engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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41Total citation count in OpenAlex
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2025: 17, 2024: 9, 2023: 10, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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90Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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