Assessment of Feature Selection for Liquefaction Prediction Based on Recursive Feature Elimination

Abstract

This paper presents a machine learning model using a random forest (RF) algorithm with the recursive feature elimination (RFE) for the soil liquefaction prediction. The prediction model is tested on 253 CPT-based field data from different earthquakes. RFE, which is one of the feature selection methods, was adopted for eliminating irrelevant features in the mentioned dataset, and then the performance of the RFE-RF (i.e., the model determined by the RFE method) and the RF models with all variables were compared in terms of their performance matrices. The primary focus of this study is to investigate the effectiveness of the feature selection algorithm approach, therefore the raw data that is a benchmark dataset was used to compare the performance of the RFE-RF. The result showed that the RFE approach improved the overall accuracy of the liquefaction prediction.

Keywords

• Liquefaction Prediction
• Feature Selection
• Machine Learning
• Recursive Feature Elimination
• Random Forest

Sıvılaşma Tahmininde Özyinelemeli Özellik Seçmeye Dayalı Faktör Seçme Yönteminin Değerlendirilmesi

Öz

Bu çalışma, zemin sıvılaşması tahmini için özyinelemeli özellik seçimi (RFE) ile rastgele orman (RF) algoritması kullanan bir makine öğrenme modeli sunmaktadır. Tahmin modeli, farklı depremlerden elde edilen 253 CPT tabanlı saha verileri üzeri kullanılarak test edilmiştir. Söz konusu veri setindeki ihtiyaç fazlası özelliklerin elimine edilmesi için özellik seçim yöntemlerinden biri olan RFE benimsenmiştir. Ardından RFE-RF'nin (yani RFE yöntemiyle belirlenen modelin) ve bütün değişkenlerin kullanıldığı RF modelin performansları performans matrisleri açısından incelenmiş ve karşılaştırılmıştır. Bu çalışmanın önceliği, öznitelik seçim algoritması yaklaşımının etkinliğini araştırmaktır, bu nedenle RFE-RF'nin performansını karşılaştırmak için bir kıyaslama veri seti olan ham veriler kullanılmıştır. Sonuç olarak, RFE yaklaşımının kullanılmasının sıvılaşma tahmin modelinin genel doğruluğunu arttırdığı görülmüştür.

Anahtar Kelimeler

• Sıvılaşma Tahmini
• Özellik Seçimi
• Makine Öğrenme
• Özyinelemeli Özellik Seçimi
• Rastgele Orman

Kaynakça

1. Andrus, R. D., & Stokoe II, K. H. (2000). Liquefaction resistance of soils from shear-wave velocity. Journal of geotechnical and geoenvironmental engineering, 126(11), 1015-1025.
2. Boulanger, R. W., & Idriss, I. M. (2014). CPT and SPT based liquefaction triggering procedures. Center for Geotechnical Modelling, Civil and Environmental Engineering, UC Davis, CA. Report No. UCD/CGM-14/01.
3. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
4. Cetin, K. O., Seed, R. B., Der Kiureghian, A., Tokimatsu, K., Harder Jr, L. F., Kayen, R. E., & Moss, R. E. (2004). Standard penetration test-based probabilistic and deterministic assessment of seismic soil liquefaction potential. Journal of geotechnical and geoenvironmental engineering, 130(12), 1314-1340.
5. Erzin, Y., & Ecemis, N. (2015). The use of neural networks for CPT-based liquefaction screening. Bulletin of Engineering Geology and the Environment, 74(1), 103-116.
6. Granitto, P. M., Furlanello, C., Biasioli, F., & Gasperi, F. (2006). Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products. Chemometrics and intelligent laboratory systems, 83(2), 83-90.
7. Gregorutti, B., Michel, B., & Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27(3), 659-678.
8. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of machine learning research, 3(Mar), 1157-1182.

9. Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46(1), 389-422.
10. Hoang, N. D., & Bui, D. T. (2018). Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study. Bulletin of Engineering Geology and the Environment, 77(1), 191-204.
11. Idriss, I. M., & Boulanger, R. W. (2008). Soil liquefaction during earthquakes. Earthquake Engineering Research Institute. Kayen, R., Moss, R. E. S., Thompson, E. M., Seed, R. B., Cetin, K. O., Kiureghian, A. D., ... & Tokimatsu, K. (2013). Shear-wave velocity–based probabilistic and deterministic assessment of seismic soil liquefaction potential. Journal of Geotechnical and Geoenvironmental Engineering, 139(3), 407-419.
12. Kohestani, V. R., Hassanlourad, M., & Ardakani, A. J. N. H. (2015). Evaluation of liquefaction potential based on CPT data using random forest. Natural Hazards, 79(2), 1079-1089.
13. Kuhn, M., & Johnson, K. (2019). Feature engineering and selection: A practical approach for predictive models. CRC Press. Kumar, D., Samui, P., Kim, D., & Singh, A. (2021). A Novel Methodology to Classify Soil Liquefaction Using Deep Learning. Geotechnical and Geological Engineering, 39(2), 1049-1058.
14. National Academies of Sciences, Engineering, and Medicine. (2016). State of the art and practice in the assessment of earthquake-induced soil liquefaction and its consequences. Washington, DC: The National Academies Press. doi, 1017226, 23474.
15. Robertson, P. K., & Wride, C. E. (1998). Evaluating cyclic liquefaction potential using the cone penetration test. Canadian geotechnical journal, 35(3), 442-459.
16. Samui, P., Kim, D., & Sitharam, T. G. (2011). Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity. Journal of applied geophysics, 73(1), 8-15.
17. Sánchez-Maroño, N., Alonso-Betanzos, A., & Tombilla-Sanromán, M. (2007, December). Filter methods for feature selection–a comparative study. In International Conference on Intelligent Data Engineering and Automated Learning (pp. 178-187). Springer, Berlin, Heidelberg.
18. Seed, H. B., & Idriss, I. M. (1971). Simplified procedure for evaluating soil liquefaction potential. Journal of the Soil Mechanics and Foundations division, 97(9), 1249-1273.
19. Shahri, A. A. (2016). Assessment and prediction of liquefaction potential using different artificial neural network models: a case study. Geotechnical and Geological Engineering, 34(3), 807-815.
20. Team, R. C. (2020). R: the R project for statistical computing. https://www.r-project.org/
21. Xue, X., & Yang, X. (2016). Seismic liquefaction potential assessed by support vector machines approaches. Bulletin of Engineering Geology and the Environment, 75(1), 153-162.