{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:26:13Z","timestamp":1776954373047,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T00:00:00Z","timestamp":1664150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2018R1A5A1025137"],"award-info":[{"award-number":["NRF-2018R1A5A1025137"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["2022R1F1A1068374"],"award-info":[{"award-number":["2022R1F1A1068374"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["NRF-2018R1A5A1025137"],"award-info":[{"award-number":["NRF-2018R1A5A1025137"]}]},{"name":"National Research Foundation of Korea (NRF)","award":["2022R1F1A1068374"],"award-info":[{"award-number":["2022R1F1A1068374"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Earthquakes cause liquefaction, which disturbs the design phase during the building construction process. The potential of earthquake-induced liquefaction was estimated initially based on analytical and numerical methods. The conventional methods face problems in providing empirical formulations in the presence of uncertainties. Accordingly, machine learning (ML) algorithms were implemented to predict the liquefaction potential. Although the ML models perform well with the specific liquefaction dataset, they fail to produce accurate results when used on other datasets. This study proposes a stacked generalization model (SGM), constructed by aggregating algorithms with the best performances, such as the multilayer perceptron regressor (MLPR), support vector regression (SVR), and linear regressor, to build an efficient prediction model to estimate the potential of earthquake-induced liquefaction on settlements. The dataset from the Korean Geotechnical Information database system and the standard penetration test conducted on the 2016 Pohang earthquake in South Korea were used. The model performance was evaluated by using the R2 score, mean-square error (MSE), standard deviation, covariance, and root-MSE. Model validation was performed to compare the performance of the proposed SGM with SVR and MLPR models. The proposed SGM yielded the best performance compared with those of the other base models.<\/jats:p>","DOI":"10.3390\/s22197292","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T03:30:37Z","timestamp":1664335837000},"page":"7292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Stacked Generalization Model to Enhance Prediction of Earthquake-Induced Soil Liquefaction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1355-710X","authenticated-orcid":false,"given":"Sri","family":"Preethaa","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641665, India"},{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0422-8374","authenticated-orcid":false,"given":"Yuvaraj","family":"Natarajan","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641665, India"},{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arun Pandian","family":"Rathinakumar","sequence":"additional","affiliation":[{"name":"Research Engineer, QpiCloud Technologies, Bangalore 560045, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9205-3836","authenticated-orcid":false,"given":"Dong-Eun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Architecture, Civil, Environment and Energy Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Young","family":"Choi","sequence":"additional","affiliation":[{"name":"Earth Turbine, 36, Dongdeok-ro 40-gil, Jung-gu, Daegu 41905, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5801-3061","authenticated-orcid":false,"given":"Young-Jun","family":"Park","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6733-8597","authenticated-orcid":false,"given":"Chang-Yong","family":"Yi","sequence":"additional","affiliation":[{"name":"Intelligent Construction Automation Center, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1785\/0120180167","article-title":"Surface deformations and rupture processes associated with the 2017 Mw 5.4 Pohang, Korea, earthquake","volume":"109","author":"Choi","year":"2019","journal-title":"Bull. 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