Ensemble learning-based classification models for slope stability analysis
DC Field | Value | Language |
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dc.contributor.author | Khanh Pham | - |
dc.contributor.author | Kim, Dongku | - |
dc.contributor.author | Park, Sangyeong | - |
dc.contributor.author | Choi, Hangseok | - |
dc.date.accessioned | 2021-08-30T04:35:06Z | - |
dc.date.available | 2021-08-30T04:35:06Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2021-01 | - |
dc.identifier.issn | 0341-8162 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/50227 | - |
dc.description.abstract | In this study, ensemble learning was applied to develop a classification model capable of accurately estimating slope stability. Two prominent ensemble techniques-parallel learning and sequential learning-were applied to implement the ensemble classifiers. Additionally, for comparison, eight versatile machine learning algorithms were utilized to formulate the single-learning classification models. These classification models were trained and evaluated on the well-established global database of slope documented from 1930 to 2005. The performance of these classification models was measured by considering the F-1 score, accuracy, receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Furthermore, K-fold cross-validation was employed to fairly assess the generalization capacity of these models. The obtained results demonstrated the advantage of ensemble classifiers over single-learning classification models. When ensemble learning was used instead of the single learning, the average F-1 score, accuracy, and AUC of the models increased by 2.17%, 1.66%, and 6.27%, respectively. In particular, the ensemble classifiers with sequential learning exhibited better performance than those with parallel learning. The ensemble classifiers on the extreme gradient boosting (XGB-CM) framework clearly provided the best performance on the test set, with the highest F-1 score, accuracy, and AUC of 0.914, 0.903, and 0.95, respectively. The excellent performance on the spatially well-distributed database along with its capacity to distribute computing indicates the significant potential applicability of the presented ensemble classifiers, particularly the XGB-CM, for landslide risk assessment and management on a global scale. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER | - |
dc.subject | LANDSLIDE SUSCEPTIBILITY | - |
dc.subject | PREDICTION | - |
dc.subject | EARTHQUAKE | - |
dc.title | Ensemble learning-based classification models for slope stability analysis | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Hangseok | - |
dc.identifier.doi | 10.1016/j.catena.2020.104886 | - |
dc.identifier.scopusid | 2-s2.0-85090403298 | - |
dc.identifier.wosid | 000583955200045 | - |
dc.identifier.bibliographicCitation | CATENA, v.196 | - |
dc.relation.isPartOf | CATENA | - |
dc.citation.title | CATENA | - |
dc.citation.volume | 196 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Geology | - |
dc.relation.journalResearchArea | Agriculture | - |
dc.relation.journalResearchArea | Water Resources | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Soil Science | - |
dc.relation.journalWebOfScienceCategory | Water Resources | - |
dc.subject.keywordPlus | LANDSLIDE SUSCEPTIBILITY | - |
dc.subject.keywordPlus | PREDICTION | - |
dc.subject.keywordPlus | EARTHQUAKE | - |
dc.subject.keywordAuthor | Ensemble classifier | - |
dc.subject.keywordAuthor | Ensemble learning | - |
dc.subject.keywordAuthor | Slope stability analysis | - |
dc.subject.keywordAuthor | Machine learning | - |
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