Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data

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dc.authorid0000-0003-2520-4395en_US
dc.authorid0000-0002-9830-8585en_US
dc.contributor.authorDemir, Selçuk
dc.contributor.authorŞahin, Emrehan Kutluğ
dc.date.accessioned2023-11-22T06:43:32Z
dc.date.available2023-11-22T06:43:32Z
dc.date.issued2022en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThis research investigates and compares the performance of three tree-based Machine Learning (ML) methods, Canonical Correlation Forest (CCF), Rotation Forest (RotFor), and Random Forest (RF), for predicting the liquefaction potential of soils based on the cone penetration test (CPT) case history datasets collected from previously published research. The ML models are trained and validated using the Stratified Random Sampling technique for training and test datasets considering three sampling ratios as 50:50, 40:60, and 70:30. In addition, a comparative example was applied to show the difference between the Stratified Random Sampling and the Simple Random Sampling technique, which is the most common probability-based sampling method, considering only a dataset. The predictive capabilities of the developed models are evaluated using Overall Accuracy, Kappa, Precision, Recall, and F-Measure values. Lastly, the Wilcoxon Signed-Rank Test and the Pearson's Correlation Coefficient are adopted to determine the statistical significance of the accuracies between the tree-based ML methods. Generally, tree-based ML methods of CCF, RotFor and, RF are found robust with respect to the variations in training sample sizes, and the performance metrics revealed that the CCF and RotFor method exhibited slightly better performance than the conventional RF method. Finally, based on the results obtained from performance assessment output, CCF and RotFor methods which are the first application in the soil liquefaction issue to the best of our knowledge are worth considering in the prediction of soil liquefaction.en_US
dc.identifier.citationDemir, S., & Sahin, E. K. (2022). Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data. Soil Dynamics and Earthquake Engineering, 154, 107130.en_US
dc.identifier.doi10.1016/j.soildyn.2021.107130
dc.identifier.endpage11en_US
dc.identifier.issn0267-7261
dc.identifier.issn1879-341X
dc.identifier.scopus2-s2.0-85121793317en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.soildyn.2021.107130
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11868
dc.identifier.volume154en_US
dc.identifier.wosWOS:000788737500003en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDemir, Selçuk
dc.institutionauthorŞahin, Emrehan Kutluğ
dc.language.isoenen_US
dc.publisherElsevier Science Ltden_US
dc.relation.ispartofSoil Dynamics and Earthquake Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLiquefactionen_US
dc.subjectMachine Learningen_US
dc.subjectRandom Forest (RF)en_US
dc.subjectSupport Vector Machinesen_US
dc.subjectArtificial Neural-Networksen_US
dc.subjectFuzzy Inference Systemen_US
dc.titleComparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT dataen_US
dc.typeArticleen_US

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