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Environmental Earth Sciences

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01.12.2023 | Original Article

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

verfasst von: Selçuk Demir, Emrehan Kutlug Sahin

Erschienen in: Environmental Earth Sciences | Ausgabe 23/2023

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Abstract

This 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 (κ). 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.

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Titel: Assessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability prediction
verfasst von: Selçuk Demir
Emrehan Kutlug Sahin
Publikationsdatum: 01.12.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 23/2023
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-023-11247-w

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