Enhanced prediction of slope stability and failure distance using hyperparameter tuning and polynomial features
Article Sidebar
Main Article Content
Abstract
This study presents an extensive analysis of slope stability using various machine learning (ML) models, focusing on
hyperparameter tuning and feature importance and model validation for predicting factor safety (FS), slope failure
distance relative to the height of the slope, and the safety level of the slope. The input parameters include cohesion
(C), internal friction angle (Phi), slope angle (Slope), and height of the slope (H). The performance of each model was
assessed using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and R-squared (R²) for
regression tasks, while classification tasks were evaluated using accuracy, precision, recall, F1-score, and Area Under
the Curve (AUC). The analysis demonstrates the efficacy of different ML models. The model that performed well in both regression and classification is the Random Forest. The use of polynomial features significantly improved the
performance of linear methods, while hyperparameter tuning greatly enhanced the performance of Support Vector and MLP models.
Article Details
References
ณัฐวุฒิ แถมเงิน, ปกรณ์ ล่องทอง, พงศศรันย์ ทองหนูนุ้ย, กนกวรรณ ละอองศรี, อนามัย เทศกะทึก, พีรพล ศิริพงศ์วุฒิกร, ณฐนนท์ เทพตะขบ และ วิริยะ มหิกุล (2023). การเรียนรู้ของเครื่องเพื่อทำนายระดับความรุนแรงของความผิดปกติของความยืดหยุ่นปอดของพนักงานโรงงาน, Science and Technology Journal Mahasarakham University, 43(2), 84-95
สุภาวิณี ขันคำ และ สิโรรัตน์ จั่นงาม (2023). แบบจำลองการพยากรณ์สำหรับปริมาณการส่งออกเครื่องเทศของประเทศไทย. Science and Technology Journal Mahasarakham University, 43(1), 74-83
Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P. M., & Derakhshani, R. (2021). Deep learning-based landslide susceptibility mapping. Scientific reports, 11(1), 24112.
Bishop, A. W. (1955). The use of the slip circle in the stability analysis of slopes. Geotechnique, 5(1), 7-17.
Fellenius, W. (1927). Erdstatische statik. MeyerandZeller.
Harabinová, S., & Panulinová, E. (2020). Impact of shear strength parameters on slope stability. MATEC Web of Conferences, 310, 00040. https://doi.org/10.1051/matecconf/202031000040
Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.
Huang, F., Xiong, H., Chen, S., Lv, Z., Huang, J., Chang, Z., & Catani, F. (2023). Slope stability prediction based on a long short-term memory neural network: Comparisons with convolutional neural networks, support vector machines and random forest models.
International Journal of Coal Science & Technology, 10(1), 18.
Huang, F., Ye, Z., Jiang, S. H., Huang, J., Chang, Z., & Chen, J. (2021). Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. Catena, 202, 105250.
Lin, S., Zheng, H., Han, C., Han, B., & Li, W. (2021). Evaluation and prediction of slope stability using machine learning approaches. Frontiers of Structural and Civil Engineering, 15(4), 821–833.
Morgenstern, N. U., & Price, V. E. (1965). The analysis of thestabilityofgeneral slipsurfaces. Géotechnique, 15(1), 79–93.
Nikoobakht, S., Azarafza, M., Akgün, H., & Derakhshani, R. (2022). Landslide susceptibility assessment by using convolutional neural network. Applied Sciences, 12(12), 5992.
Pham, B.T., Vu, V. D., Costache, R., Phong, T. V., Ngo, T. Q., Tran, T. H., ... & Prakash, I. (2022). Landslide susceptibility mappingusingstate-of-the-art machine learning ensembles. Geocarto International, 37(18), 5175–5200.
Singh, S. K., & Chakravarty, D. (2023). Assessment of slope stability using classification and regression algorithms subjected to internal and external factors. Archives of Mining Sciences, 68(1), 87–102.
Spencer, E. (1967). A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique, 17(1), 11-26.
Tien Bui, D., Moayedi, H., Gör, M., Jaafari, A., & Foong, L. K. (2019). Predicting slope stability failure through machine learning paradigms. ISPRS International Journal of Geo-Information, 8(9), 395.
Wang, Y., Sun, D., Wen, H., Zhang, H., & Zhang, F. (2020). Comparison of random forest model and frequency ratio model for landslide susceptibility mapping (LSM) in Yunyang County (Chongqing, China). International Journal of Environmental Research and Public Health, 17(12), 4206.
World Bank. (2020). Global landslide hazard 1 [Data set]. World Bank Data Catalog. https://datacatalog.worldbank.org/dataset/global-landslide-hazard-1
World Health Organization. (2023). Landslides. https://www.who.int/health-topics/landslides
WSDOT. (2013). Geotechnical design manual M 46-03. Washington State Department of Transportation.
