Assessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability prediction

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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.accessioned2024-05-10T10:44:55Z
dc.date.available2024-05-10T10:44:55Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThis paper presents DeepBoost based classification model for the slope stability problem, wherein an extensive dataset consisting of six features is used. The developed DeepBoost model is trained and tested with 444 stable and unstable slope cases. For comparison, the predictive performance of DeepBoost is systematically compared with the other state-of-the-art ML algorithms, i.e., Adaptive Boosting (AdaBoost.M1) and Support Vector Machine (SVM) based on the well-established confusion matrix, which contains the known metrics of Accuracy (Acc), Precision (P), Recall (R), F1-Score (F), and Kappa Score (kappa). Furthermore, three hyperparameter optimization approaches, Grid Search (GS), Random Search (RS), and Bayesian Optimization (BO), have been integrated for tuning the hyperparameters of the DeepBoost and the other models to achieve the best results. Based on the comparative analysis, it was found that BO optimized DeepBoost model achieved the best performance score and accurately detected and classified all types of slope stability scenarios. Also, Bayesian optimized models performed better than GS and RS optimized ones. As a result, the comparison results of the developed DeepBoost model with the other models reveal that DeepBoost exhibited superior performance as compared to the other algorithms in the case of BO with an accuracy of Acc = 96.97% for DeepBoost, Acc = 95.45% for AdaBoostM1, and Acc = 90.91% for SVM.en_US
dc.identifier.citationDemir, S., & Sahin, E. K. (2023). Assessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability prediction. Environmental Earth Sciences, 82(23), 562.en_US
dc.identifier.doi10.1007/s12665-023-11247-w
dc.identifier.endpage15en_US
dc.identifier.issn1866-6280
dc.identifier.issn1866-6299
dc.identifier.issue562en_US
dc.identifier.scopus2-s2.0-85176043716en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s12665-023-11247-w
dc.identifier.urihttps://hdl.handle.net/20.500.12491/12139
dc.identifier.volume82en_US
dc.identifier.wosWOS:001102616600003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDemir, Selçuk
dc.institutionauthorŞahin, Emrehan Kutluğ
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironmental Earth Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaBoost.M1en_US
dc.subjectBayesian Optimizationen_US
dc.subjectDeepBoosten_US
dc.subjectSlope Stabilityen_US
dc.subjectStrengthen_US
dc.subjectChartsen_US
dc.titleAssessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability predictionen_US
dc.typeArticleen_US

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