Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing
| dc.authorid | 0000-0003-2520-4395 | en_US |
| dc.authorid | 0000-0002-9830-8585 | en_US |
| dc.contributor.author | Demir, Selçuk | |
| dc.contributor.author | Şahin, Emrehan Kutluğ | |
| dc.date.accessioned | 2023-11-28T10:55:20Z | |
| dc.date.available | 2023-11-28T10:55:20Z | |
| dc.date.issued | 2022 | en_US |
| dc.department | BAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü | en_US |
| dc.description.abstract | Liquefaction prediction is an important issue in the seismic design of engineering structures, and research on this topic has been continuing in current literature using different methods, including experimental, numerical, or soft computing. In this paper, three robust machine learning (ML) algorithms are applied to predict soil liquefaction using a set of 411 shear wave velocity case records. The Genetic Algorithm (GA) based feature selection (FS) and parameter optimization of Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGBoost) algorithms are utilized to improve the accuracy of the liquefaction prediction models. Simple Random Sampling (SRS) and Stratified Random Sampling (StrRS) are used for data sampling, and also SMOTE algorithm are applied to prepare the balanced training sets. The results of robust ML algorithms are assessed based on well-known five performance matrices, namely Accuracy (Acc), Kappa, Precision, Recall, and F-Measure. Evaluation of the results is made separately for each ML algorithm considering sampling data generated from SRS, StrRS, and SMOTE. As a result, the XGBoost model is more accurate (Acc = 96%) than RF (Acc = 93%) and SVM (Acc = 91%) in the case of the SMOTE algorithm. This study reveals the superiority of the XGBoost algorithm in the liquefaction prediction and shows how the accuracy measures tend to improve when the predictive models are trained using balanced samples with StrRS and SMOTE sampling strategies. | en_US |
| dc.identifier.citation | Demir, S., & Şahin, E. K. (2022). Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing. Environmental Earth Sciences, 81(18), 459. | en_US |
| dc.identifier.doi | 10.1007/s12665-022-10578-4 | |
| dc.identifier.endpage | 17 | en_US |
| dc.identifier.issn | 1866-6280 | |
| dc.identifier.issn | 1866-6299 | |
| dc.identifier.issue | 18 | en_US |
| dc.identifier.scopus | 2-s2.0-85138466566 | en_US |
| dc.identifier.scopusquality | Q2 | en_US |
| dc.identifier.startpage | 1 | en_US |
| dc.identifier.uri | http://dx.doi.org/10.1007/s12665-022-10578-4 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12491/11888 | |
| dc.identifier.volume | 81 | en_US |
| dc.identifier.wos | WOS:000855786000002 | en_US |
| dc.identifier.wosquality | Q2 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.institutionauthor | Demir, Selçuk | |
| dc.institutionauthor | Şahin, Emrehan Kutluğ | |
| dc.language.iso | en | en_US |
| dc.publisher | Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing | en_US |
| dc.relation.ispartof | Environmental Earth Sciences | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Liquefaction Prediction | en_US |
| dc.subject | SMOTE | en_US |
| dc.subject | Genetic Algorithm | en_US |
| dc.subject | Neural-Network | en_US |
| dc.subject | Coefficient | en_US |
| dc.subject | Earthquake | en_US |
| dc.title | Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing | en_US |
| dc.type | Article | en_US |
