Details
| Original language | English |
|---|---|
| Article number | 2142004 |
| Number of pages | 25 |
| Journal | International Journal of Computational Methods |
| Volume | 19 |
| Issue number | 8 |
| Early online date | 2 Mar 2022 |
| Publication status | Published - 1 Oct 2022 |
Abstract
The study and prediction of soil liquefaction is an important and complex issue in geotechnical earthquake engineering. This paper attempts to compare the predictability of soil liquefaction potential between several machine learning classification models, which includes some tree-based classifiers, multilayer perceptron (MLP) neural networks, Support Vector Machine (SVM), some state-of-the-art ensemble methods, K nearest neighbors method, classical Naive Bayesian classifier and logistic regression. Three data sets covering shear-wave velocity measurements, cone penetration testing (CPT), and real historic earthquakes cases are employed to train and evaluate the machine learning classifiers. In order to make the best use of large varieties of statistical and machine learning classification algorithms, it is necessary to give a comparative evaluation of the model performance before model selection and offer advice on a unified stable model for all sorts of collected datasets. In the comparative study, data preprocessing is first performed to ensure the dataset into all machine models is of good quality. Then all three datasets with different input features are passed into the machine learning algorithms to obtain its confusion matrix and some evaluation indices. Reliable assessment of model performance is done through a repeated sub-sampling process. Experimental results are also supported by ROC curves. The results of this study indicated that although most machine learning methods are able to represent the complex relationship between seismic proper seismic properties of soils and corresponding liquefaction potential, ensemble learning has achieved more successful results in all three datasets test and can be a fairly promising approach on prediction of earthquake-induced soil liquefaction.
Keywords
- classification, Earthquake induced, extra-tree, machine learning, prediction, repeated sub-sampling, soil liquefaction, statistical inference
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science (miscellaneous)
- Mathematics(all)
- Computational Mathematics
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In: International Journal of Computational Methods, Vol. 19, No. 8, 2142004, 01.10.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Predicting Earthquake-Induced Soil Liquefaction Based on Machine Learning Classifiers
T2 - A Comparative Multi-Dataset Study
AU - Guo, Hongwei
AU - Zhuang, Xiaoying
AU - Chen, Jianfeng
AU - Zhu, He Hua
PY - 2022/10/1
Y1 - 2022/10/1
N2 - The study and prediction of soil liquefaction is an important and complex issue in geotechnical earthquake engineering. This paper attempts to compare the predictability of soil liquefaction potential between several machine learning classification models, which includes some tree-based classifiers, multilayer perceptron (MLP) neural networks, Support Vector Machine (SVM), some state-of-the-art ensemble methods, K nearest neighbors method, classical Naive Bayesian classifier and logistic regression. Three data sets covering shear-wave velocity measurements, cone penetration testing (CPT), and real historic earthquakes cases are employed to train and evaluate the machine learning classifiers. In order to make the best use of large varieties of statistical and machine learning classification algorithms, it is necessary to give a comparative evaluation of the model performance before model selection and offer advice on a unified stable model for all sorts of collected datasets. In the comparative study, data preprocessing is first performed to ensure the dataset into all machine models is of good quality. Then all three datasets with different input features are passed into the machine learning algorithms to obtain its confusion matrix and some evaluation indices. Reliable assessment of model performance is done through a repeated sub-sampling process. Experimental results are also supported by ROC curves. The results of this study indicated that although most machine learning methods are able to represent the complex relationship between seismic proper seismic properties of soils and corresponding liquefaction potential, ensemble learning has achieved more successful results in all three datasets test and can be a fairly promising approach on prediction of earthquake-induced soil liquefaction.
AB - The study and prediction of soil liquefaction is an important and complex issue in geotechnical earthquake engineering. This paper attempts to compare the predictability of soil liquefaction potential between several machine learning classification models, which includes some tree-based classifiers, multilayer perceptron (MLP) neural networks, Support Vector Machine (SVM), some state-of-the-art ensemble methods, K nearest neighbors method, classical Naive Bayesian classifier and logistic regression. Three data sets covering shear-wave velocity measurements, cone penetration testing (CPT), and real historic earthquakes cases are employed to train and evaluate the machine learning classifiers. In order to make the best use of large varieties of statistical and machine learning classification algorithms, it is necessary to give a comparative evaluation of the model performance before model selection and offer advice on a unified stable model for all sorts of collected datasets. In the comparative study, data preprocessing is first performed to ensure the dataset into all machine models is of good quality. Then all three datasets with different input features are passed into the machine learning algorithms to obtain its confusion matrix and some evaluation indices. Reliable assessment of model performance is done through a repeated sub-sampling process. Experimental results are also supported by ROC curves. The results of this study indicated that although most machine learning methods are able to represent the complex relationship between seismic proper seismic properties of soils and corresponding liquefaction potential, ensemble learning has achieved more successful results in all three datasets test and can be a fairly promising approach on prediction of earthquake-induced soil liquefaction.
KW - classification
KW - Earthquake induced
KW - extra-tree
KW - machine learning
KW - prediction
KW - repeated sub-sampling
KW - soil liquefaction
KW - statistical inference
UR - http://www.scopus.com/inward/record.url?scp=85126033119&partnerID=8YFLogxK
U2 - 10.1142/S0219876221420044
DO - 10.1142/S0219876221420044
M3 - Article
AN - SCOPUS:85126033119
VL - 19
JO - International Journal of Computational Methods
JF - International Journal of Computational Methods
SN - 0219-8762
IS - 8
M1 - 2142004
ER -