Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques
<p>Landslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods rem...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1852022298660306944 |
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| author | Zuleide Ferreira (20832508) |
| author2 | Bruna Almeida (12461880) Ana Cristina Costa (3313470) Manoel do Couto Fernandes (20832511) Pedro Cabral (4253539) |
| author2_role | author author author author |
| author_facet | Zuleide Ferreira (20832508) Bruna Almeida (12461880) Ana Cristina Costa (3313470) Manoel do Couto Fernandes (20832511) Pedro Cabral (4253539) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zuleide Ferreira (20832508) Bruna Almeida (12461880) Ana Cristina Costa (3313470) Manoel do Couto Fernandes (20832511) Pedro Cabral (4253539) |
| dc.date.none.fl_str_mv | 2025-03-05T16:00:08Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.28541272.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Insights_into_landslide_susceptibility_a_comparative_evaluation_of_multi-criteria_analysis_and_machine_learning_techniques/28541272 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Neuroscience Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Geospatial modelling landslide-prone areas hazard mapping disaster risk reduction climate change |
| dc.title.none.fl_str_mv | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Landslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. The ML models used 29 influencing factors, encompassing geographic, geological, climatic, and anthropogenic variables, where feature importance analysis and hyperparameter tuning were applied to identify the most significant predictors. RF achieved the highest performance, with an accuracy of 0.94, ROC AUC of 0.98, and F1 score of 0.94. SVM and LR also performed well, with ROC AUCs of 0.96 and 0.95 and F1 scores of 0.92 and 0.89, respectively. Conversely, MCA showed lower results, with an accuracy of 0.41, ROC AUC of 0.41, and F1 score of 0.55. We attribute RF’s robustness to its adaptability to diverse variable types, reduced overfitting risk, and high predictive accuracy. These findings underscore RF’s strength in LSM and highlight ML’s potential to support urban planning and mitigate risks in landslide-prone areas.</p> <p></p><p>Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.</p><p>MCA failed to identify non-landslide areas, highlighting its limitations.</p><p>ML overcomes traditional MCA in landslide susceptibility mapping.</p><p>RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.</p><p>ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.</p><p></p> <p>Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.</p> <p>MCA failed to identify non-landslide areas, highlighting its limitations.</p> <p>ML overcomes traditional MCA in landslide susceptibility mapping.</p> <p>RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.</p> <p>ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_eec9cd12f8e9cc1cb5f770542fe8ce21 |
| identifier_str_mv | 10.6084/m9.figshare.28541272.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28541272 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniquesZuleide Ferreira (20832508)Bruna Almeida (12461880)Ana Cristina Costa (3313470)Manoel do Couto Fernandes (20832511)Pedro Cabral (4253539)NeuroscienceBiotechnologySociologySpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedGeospatial modellinglandslide-prone areashazard mappingdisaster risk reductionclimate change<p>Landslides threaten communities worldwide, resulting in financial, environmental, and human losses. Although some studies have employed machine learning (ML) algorithms and multi-criteria analysis (MCA) for landslide susceptibility mapping (LSM), comparative evaluations of these methods remain scarce, particularly regarding predictor importance, performance metrics, and hyperparameter optimization. This research addresses these gaps by comparing logistic regression (LR), random forest (RF), support vector machines (SVM), and MCA, focusing on landslide susceptibility in Petrópolis, Brazil. The ML models used 29 influencing factors, encompassing geographic, geological, climatic, and anthropogenic variables, where feature importance analysis and hyperparameter tuning were applied to identify the most significant predictors. RF achieved the highest performance, with an accuracy of 0.94, ROC AUC of 0.98, and F1 score of 0.94. SVM and LR also performed well, with ROC AUCs of 0.96 and 0.95 and F1 scores of 0.92 and 0.89, respectively. Conversely, MCA showed lower results, with an accuracy of 0.41, ROC AUC of 0.41, and F1 score of 0.55. We attribute RF’s robustness to its adaptability to diverse variable types, reduced overfitting risk, and high predictive accuracy. These findings underscore RF’s strength in LSM and highlight ML’s potential to support urban planning and mitigate risks in landslide-prone areas.</p> <p></p><p>Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.</p><p>MCA failed to identify non-landslide areas, highlighting its limitations.</p><p>ML overcomes traditional MCA in landslide susceptibility mapping.</p><p>RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.</p><p>ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.</p><p></p> <p>Effective landslide susceptibility analysis is essential for anticipating and mitigating risks.</p> <p>MCA failed to identify non-landslide areas, highlighting its limitations.</p> <p>ML overcomes traditional MCA in landslide susceptibility mapping.</p> <p>RF achieved the highest prediction accuracy for landslide susceptibility, outperforming other methods.</p> <p>ML-based landslide susceptibility mapping ranks susceptibility factors more effectively.</p>2025-03-05T16:00:08ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.28541272.v1https://figshare.com/articles/dataset/Insights_into_landslide_susceptibility_a_comparative_evaluation_of_multi-criteria_analysis_and_machine_learning_techniques/28541272CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285412722025-03-05T16:00:08Z |
| spellingShingle | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques Zuleide Ferreira (20832508) Neuroscience Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Geospatial modelling landslide-prone areas hazard mapping disaster risk reduction climate change |
| status_str | publishedVersion |
| title | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| title_full | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| title_fullStr | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| title_full_unstemmed | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| title_short | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| title_sort | Insights into landslide susceptibility: a comparative evaluation of multi-criteria analysis and machine learning techniques |
| topic | Neuroscience Biotechnology Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Geospatial modelling landslide-prone areas hazard mapping disaster risk reduction climate change |