A comparative study of ensemble learning algorithms for the classification of landslide activity using vegetation anomalies indicator (VAI): a case study of Kundasang, Sabah Article Swipe
Mohd Radhie Mohd Salleh
,
Muhammad Zulkarnain Abdul Rahman
,
Zamri Ismail
,
Mohd Faisal Abdul Khanan
,
Radzuan Sa’ari
,
Ahmad Razali Yusoff
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s44288-025-00171-0
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1007/s44288-025-00171-0
Landslide activity classification is crucial for disaster risk management and mitigation. This study explores the effectiveness of ensemble learning algorithms in classifying landslide activities using Vegetation Anomalies Indicator (VAI) within Kundasang, Sabah, Malaysia. Seven groups of VAIs were selected based on their relevance to the geo-environmental conditions of the study area. The analysis incorporated a comprehensive dataset of landslide occurrences, with 70% of the sites designated for training and 30% for validation. Ensemble learning algorithms, specifically boosting and bagging were employed to classify landslide activity. The performance of these algorithms was evaluated against a landslide inventory map using several metrics: producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and kappa coefficient (κ). Results demonstrate that Decision Tree (DT) and Stochastic Gradient Boosting (SGB) achieved the highest OA of 80.0% and 75.9% at a 1-m resolution, respectively. For bagging algorithms, Random Forest (RF) outperformed BAGGED CART with an OA of 81.9% at a 1-m resolution. Additionally, the κ indicated a substantial agreement for both ensemble methods, with values reaching up to 0.728 for RF. These findings underscore the potential of utilizing VAIs combined with ensemble learning for effective landslide activity classification, contributing valuable insights for improving landslide risk assessment and management practices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s44288-025-00171-0
- https://link.springer.com/content/pdf/10.1007/s44288-025-00171-0.pdf
- OA Status
- diamond
- References
- 78
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411185406
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411185406Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s44288-025-00171-0Digital Object Identifier
- Title
-
A comparative study of ensemble learning algorithms for the classification of landslide activity using vegetation anomalies indicator (VAI): a case study of Kundasang, SabahWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-10Full publication date if available
- Authors
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Mohd Radhie Mohd Salleh, Muhammad Zulkarnain Abdul Rahman, Zamri Ismail, Mohd Faisal Abdul Khanan, Radzuan Sa’ari, Ahmad Razali YusoffList of authors in order
- Landing page
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https://doi.org/10.1007/s44288-025-00171-0Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s44288-025-00171-0.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s44288-025-00171-0.pdfDirect OA link when available
- Concepts
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Landslide, Vegetation (pathology), Ensemble learning, Artificial intelligence, Computer science, Algorithm, Geography, Pattern recognition (psychology), Machine learning, Geology, Seismology, Pathology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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78Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| primary_location.id | doi:10.1007/s44288-025-00171-0 |
| primary_location.is_oa | True |
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| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2948-1589 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Discover Geoscience |
| primary_location.source.host_organization | https://openalex.org/P4310319900 |
| primary_location.source.host_organization_name | Springer Science+Business Media |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319900, https://openalex.org/P4310319965 |
| primary_location.source.host_organization_lineage_names | Springer Science+Business Media, Springer Nature |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007/s44288-025-00171-0.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Discover Geoscience |
| primary_location.landing_page_url | https://doi.org/10.1007/s44288-025-00171-0 |
| publication_date | 2025-06-10 |
| publication_year | 2025 |
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| abstract_inverted_index.at | 133, 152 |
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| abstract_inverted_index.on | 41 |
| abstract_inverted_index.to | 44, 82, 171 |
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| abstract_inverted_index.κ | 158 |
| abstract_inverted_index.1-m | 135, 154 |
| abstract_inverted_index.30% | 70 |
| abstract_inverted_index.70% | 62 |
| abstract_inverted_index.For | 138 |
| abstract_inverted_index.RF. | 174 |
| abstract_inverted_index.The | 52, 86 |
| abstract_inverted_index.and | 10, 69, 78, 110, 120, 131, 200 |
| abstract_inverted_index.for | 6, 67, 71, 163, 173, 187, 195 |
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| abstract_inverted_index.with | 61, 147, 167, 184 |
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| abstract_inverted_index.(PA), | 103 |
| abstract_inverted_index.(SGB) | 124 |
| abstract_inverted_index.(UA), | 106 |
| abstract_inverted_index.(VAI) | 29 |
| abstract_inverted_index.(κ). | 113 |
| abstract_inverted_index.0.728 | 172 |
| abstract_inverted_index.75.9% | 132 |
| abstract_inverted_index.80.0% | 130 |
| abstract_inverted_index.81.9% | 151 |
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| abstract_inverted_index.These | 175 |
| abstract_inverted_index.area. | 51 |
| abstract_inverted_index.based | 40 |
| abstract_inverted_index.kappa | 111 |
| abstract_inverted_index.sites | 65 |
| abstract_inverted_index.study | 13, 50 |
| abstract_inverted_index.their | 42 |
| abstract_inverted_index.these | 89 |
| abstract_inverted_index.using | 25, 98 |
| abstract_inverted_index.BAGGED | 145 |
| abstract_inverted_index.Forest | 142 |
| abstract_inverted_index.Random | 141 |
| abstract_inverted_index.Sabah, | 32 |
| abstract_inverted_index.groups | 35 |
| abstract_inverted_index.values | 168 |
| abstract_inverted_index.within | 30 |
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| abstract_inverted_index.Decision | 117 |
| abstract_inverted_index.Ensemble | 73 |
| abstract_inverted_index.Gradient | 122 |
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| abstract_inverted_index.achieved | 125 |
| abstract_inverted_index.activity | 2, 190 |
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| abstract_inverted_index.classify | 83 |
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| abstract_inverted_index.findings | 176 |
| abstract_inverted_index.insights | 194 |
| abstract_inverted_index.learning | 19, 74, 186 |
| abstract_inverted_index.methods, | 166 |
| abstract_inverted_index.metrics: | 100 |
| abstract_inverted_index.producer | 101 |
| abstract_inverted_index.reaching | 169 |
| abstract_inverted_index.selected | 39 |
| abstract_inverted_index.training | 68 |
| abstract_inverted_index.valuable | 193 |
| abstract_inverted_index.Anomalies | 27 |
| abstract_inverted_index.Indicator | 28 |
| abstract_inverted_index.Landslide | 1 |
| abstract_inverted_index.Malaysia. | 33 |
| abstract_inverted_index.activity. | 85 |
| abstract_inverted_index.agreement | 162 |
| abstract_inverted_index.effective | 188 |
| abstract_inverted_index.evaluated | 92 |
| abstract_inverted_index.improving | 196 |
| abstract_inverted_index.indicated | 159 |
| abstract_inverted_index.inventory | 96 |
| abstract_inverted_index.landslide | 23, 59, 84, 95, 189, 197 |
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| abstract_inverted_index.utilizing | 181 |
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| abstract_inverted_index.practices. | 202 |
| abstract_inverted_index.underscore | 177 |
| abstract_inverted_index.algorithms, | 75, 140 |
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| abstract_inverted_index.demonstrate | 115 |
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| abstract_inverted_index.resolution. | 155 |
| abstract_inverted_index.substantial | 161 |
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| abstract_inverted_index.specifically | 76 |
| abstract_inverted_index.Additionally, | 156 |
| abstract_inverted_index.comprehensive | 56 |
| abstract_inverted_index.effectiveness | 16 |
| abstract_inverted_index.respectively. | 137 |
| abstract_inverted_index.classification | 3 |
| abstract_inverted_index.classification, | 191 |
| abstract_inverted_index.geo-environmental | 46 |
| cited_by_percentile_year | |
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| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.29545012 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |