Publication:
Landslide Susceptibility Mapping with Stacking Ensemble Machine Learning

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dc.citedby1
dc.contributor.authorSolihin M.I.en_US
dc.contributor.authorYantoen_US
dc.contributor.authorHayder G.en_US
dc.contributor.authorMaarif H.A.-Q.en_US
dc.contributor.authorid16644075500en_US
dc.contributor.authorid56685916900en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid45561462400en_US
dc.date.accessioned2024-10-14T03:21:15Z
dc.date.available2024-10-14T03:21:15Z
dc.date.issued2023
dc.description.abstractLandslide susceptibility mapping (LSM) is an important preliminary effort to reduce the risk and harshness of landslide disasters. While numerous methods have been proposed, machine learning (ML) is the most popular approach that has been applied across the globe. One of the prominent methods to improve machine learning accuracy is by using ensemble method which basically employs multiple base models. In this paper, the stacking ensemble method is used to increase the accuracy of the machine learning model for LSM where the base (first-level) learners use five ML algorithms namely decision tree (DT), k-nearest neighbor (KNN), AdaBoost, extreme gradient boosting (XGB) and random forest (RF). The second-level learner uses logistic regression (LR) to aggregate the final prediction output. The landslide data together with its conditioning factors (feature variables) collected from three districts in the Central Java Province, Indonesia, has been used as the case study for the LSM. As the data are extremely imbalanced, Adaptive Synthetic (ADASYN) resampling technique was picked to balance the data between two classes, i.e., landslide and non-landslide. This is because the occurrence of non-slide incidents is much more than the landslide. The evaluation results of the LSM performance show that the proposed stacking ensemble ML improves the overall accuracy of the individual base ML model even when it is compared with RF which is naturally also ensemble ML. � 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-031-26580-8_7
dc.identifier.epage40
dc.identifier.scopus2-s2.0-85161563401
dc.identifier.spage35
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85161563401&doi=10.1007%2f978-3-031-26580-8_7&partnerID=40&md5=686bcec5488ea3042278e9bf8fcf563b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34631
dc.pagecount5
dc.publisherSpringer Natureen_US
dc.sourceScopus
dc.sourcetitleAdvances in Science, Technology and Innovation
dc.subjectImbalanced classification
dc.subjectLandslide susceptibility mapping
dc.subjectMachine learning algorithms
dc.subjectStacking ensemble
dc.titleLandslide Susceptibility Mapping with Stacking Ensemble Machine Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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