{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:52:29Z","timestamp":1782485549457,"version":"3.54.5"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3004304"],"award-info":[{"award-number":["2022YFC3004304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52378209"],"award-info":[{"award-number":["52378209"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2568216"],"award-info":[{"award-number":["U2568216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,10]]},"DOI":"10.1016\/j.asoc.2026.115790","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:29:19Z","timestamp":1781764159000},"page":"115790","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["A near-real-time adaptive framework for ground motion prediction using incremental gradient boosting"],"prefix":"10.1016","volume":"202","author":[{"given":"Jianguang","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqiang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhong","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1844-530X","authenticated-orcid":false,"given":"Tianxing","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-5343","authenticated-orcid":false,"given":"Chang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115790_bib1","doi-asserted-by":"crossref","first-page":"4341","DOI":"10.5194\/nhess-24-4341-2024","article-title":"Review article: Physical vulnerability database for critical infrastructure hazard risk assessments - a systematic review and data collection","volume":"24","author":"Nirandjan","year":"2024","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"10.1016\/j.asoc.2026.115790_bib2","article-title":"Seismic resilience in critical infrastructures: a power station preparedness case study","volume":"14","author":"Sherzer","year":"2024","journal-title":"Appl. Sci."},{"key":"10.1016\/j.asoc.2026.115790_bib3","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.ress.2016.04.007","article-title":"A geographical and multi-criteria vulnerability assessment of transportation networks against extreme earthquakes","volume":"153","author":"Kermanshah","year":"2016","journal-title":"Reliab. Eng. & Syst. Saf."},{"key":"10.1016\/j.asoc.2026.115790_bib4","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1080\/13632469.2023.2183719","article-title":"Correlation analysis between observed loss of function in health facilities and seismic actions","volume":"28","author":"Rivera-Rogel","year":"2024","journal-title":"J. Earthq. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib5","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1193\/070913EQS198M","article-title":"Summary of the ASK14 ground motion relation for active crustal regions","volume":"30","author":"Abrahamson","year":"2014","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib6","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1193\/070113EQS184M","article-title":"NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes","volume":"30","author":"Boore","year":"2014","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib7","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1193\/062913EQS175M","article-title":"NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra","volume":"30","author":"Campbell","year":"2014","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib8","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1193\/072813EQS219M","article-title":"Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra","volume":"30","author":"Chiou","year":"2014","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib9","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s00024-010-0161-6","article-title":"CyberShake: A Physics-Based Seismic Hazard Model for Southern California","volume":"168","author":"Graves","year":"2011","journal-title":"Pure Appl. Geophys."},{"key":"10.1016\/j.asoc.2026.115790_bib10","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1785\/0120100233","article-title":"Testing long-period ground-motion simulations of scenario earthquakes using the Mw 7.2 El Mayor-Cucapah mainshock: Evaluation of finite-fault rupture characterization and 3D seismic velocity models","volume":"101","author":"Graves","year":"2011","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib11","doi-asserted-by":"crossref","first-page":"2136","DOI":"10.1785\/0120160088","article-title":"Kinematic ground-motion simulations on rough faults including effects of 3D stochastic velocity perturbations","volume":"106","author":"Graves","year":"2016","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib12","doi-asserted-by":"crossref","first-page":"11918","DOI":"10.1016\/j.eswa.2009.04.011","article-title":"Fault-tree analysis for liquefied natural gas terminal emergency shutdown system","volume":"36","author":"Cheng","year":"2009","journal-title":"Expert. Sys Appl."},{"key":"10.1016\/j.asoc.2026.115790_bib13","doi-asserted-by":"crossref","first-page":"176","DOI":"10.3141\/1882-21","article-title":"Simulation model for real-time emergency vehicle dispatching and routing","author":"Haghani","year":"2004","journal-title":"Transp. Res. Rec. Natl. Res. Counc."},{"key":"10.1016\/j.asoc.2026.115790_bib14","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1016\/j.asoc.2015.12.013","article-title":"Prediction of ground motion parameters using randomized ANFIS (RANFIS)","volume":"40","author":"Thomas","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115790_bib15","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.asoc.2018.03.052","article-title":"Improving the prediction of ground motion parameters based on an efficient bagging ensemble model of M5\u2032 and CART algorithms","volume":"68","author":"Hamze-Ziabari","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115790_bib16","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.asoc.2019.03.029","article-title":"Predicting the principal strong ground motion parameters: A deep learning approach","volume":"80","author":"Derakhshani","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115790_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.111195","article-title":"A simple and flexible bootstrap-based framework to quantify epistemic uncertainty of ground motion models by light gradient boosting machine","volume":"152","author":"Wen","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115790_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112730","article-title":"Conditional generative adversarial networks for the generation of strong ground motion parameters using KiK-net ground motion records","volume":"170","author":"Ba","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115790_bib19","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1007\/s11069-023-06230-4","article-title":"Application of a new machine learning model to improve earthquake ground motion predictions","volume":"120","author":"Joshi","year":"2024","journal-title":"Nat. HAZARDS"},{"key":"10.1016\/j.asoc.2026.115790_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.soildyn.2023.108391","article-title":"Ground motion prediction model for shallow crustal earthquakes in Japan based on XGBoost with Bayesian optimization","volume":"177","author":"Dang","year":"2024","journal-title":"SOIL Dyn. Earthq. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib21","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1785\/0120210244","article-title":"Machine-Learning-Based Surface Ground-Motion Prediction Models for South Korea with Low-to-Moderate Seismicity","volume":"112","author":"Seo","year":"2022","journal-title":"Bull. SEISMOLOGICAL Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib22","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1007\/s11069-023-06257-7","article-title":"Peak ground acceleration prediction using supervised machine learning algorithm for the seismically hazardous Kachchh rift zone, Gujarat, India","volume":"120","author":"Mandal","year":"2024","journal-title":"Nat. HAZARDS"},{"key":"10.1016\/j.asoc.2026.115790_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.powtec.2025.121604","article-title":"A novel nonlinear regression equation for predicting critical flow velocity in slurry transport: A comparative study with advanced machine learning methods and classical empirical correlations","volume":"467","author":"Dindar","year":"2026","journal-title":"Powder Technol."},{"key":"10.1016\/j.asoc.2026.115790_bib24","article-title":"State of the art: Application of machine learning in ground motion modeling","volume":"149","author":"Alidadi","year":"2025","journal-title":"Eng. Appl. Artif. \u2121LIGENCE"},{"key":"10.1016\/j.asoc.2026.115790_bib25","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1111\/mice.13340","article-title":"A hybrid non-parametric ground motion model of power spectral density based on machine learning","volume":"40","author":"Ding","year":"2025","journal-title":"COMPUTER-AIDED Civ. Infrastruct. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.soildyn.2025.109274","article-title":"Ground-motion generations using Multi-label Conditional Embedding-conditional Denoising Diffusion Probabilistic Model (ML-cDDPM)","volume":"191","author":"Huang","year":"2025","journal-title":"SOIL Dyn. Earthq. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib27","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s10712-008-9046-y","article-title":"A Survey of Techniques for Predicting Earthquake Ground Motions for Engineering Purposes","volume":"29","author":"Douglas","year":"2008","journal-title":"SURVEYS GEOPHYSICS"},{"key":"10.1016\/j.asoc.2026.115790_bib28","first-page":"203","article-title":"Recent and future developments in earthquake ground motion estimation, EARTH-SCIENC","volume":"160","author":"Douglas","year":"2016","journal-title":"E Rev."},{"key":"10.1016\/j.asoc.2026.115790_bib29","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1177\/87552930211030298","article-title":"ShakeMap operations, policies, and procedures","volume":"38","author":"Wald","year":"2022","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib30","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1193\/1.2428313","article-title":"Post earthquake prioritization of bridge inspections","volume":"23","author":"Ranf","year":"2007","journal-title":"Earthq. Spectra"},{"key":"10.1016\/j.asoc.2026.115790_bib31","author":"Liu","year":"2024","journal-title":"DataSet Strong Ground Motion Parameters Magnit. M4. 0 Earthq. China 2020"},{"key":"10.1016\/j.asoc.2026.115790_bib32","unstructured":"Standardization Administration of China and State Administration for Market Regulation, The Chinese Seismic Intensity Scale, Standards Press of China, 2020. \u3008https:\/\/openstd.samr.gov.cn\/bzgk\/gb\/newGbInfo?hcno=6FD8F9071FAC980D5B2A636A9EA79DE9&refer=outter\u3009."},{"key":"10.1016\/j.asoc.2026.115790_bib33","doi-asserted-by":"crossref","first-page":"5556","DOI":"10.1029\/2019GC008515","article-title":"The Generic Mapping Tools version 6","volume":"20","author":"Wessel","year":"2019","journal-title":"Geochem. Geophys. Geosystems"},{"key":"10.1016\/j.asoc.2026.115790_bib34","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Stat."},{"key":"10.1016\/j.asoc.2026.115790_bib35","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"3149","article-title":"LightGBM: a highly efficient gradient boosting decision tree","author":"Ke","year":"2017"},{"key":"10.1016\/j.asoc.2026.115790_bib36","first-page":"466","article-title":"Is Deep Learning on Tabular Data Enough? An Assessment","volume":"13","author":"Fayaz","year":"2022","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"10.1016\/j.asoc.2026.115790_bib37","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","article-title":"Tabular data: Deep learning is not all you need","volume":"81","author":"Shwartz-Ziv","year":"2022","journal-title":"Inf. Fusion."},{"key":"10.1016\/j.asoc.2026.115790_bib38","series-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems","article-title":"Why do tree-based models still outperform deep learning on typical tabular data?","author":"Grinsztajn","year":"2022"},{"key":"10.1016\/j.asoc.2026.115790_bib39","series-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","article-title":"When do neural nets outperform boosted trees on tabular data?","author":"McElfresh","year":"2023"},{"key":"10.1016\/j.asoc.2026.115790_bib40","first-page":"89","article-title":"Statistical properties of the population stability index","author":"Yurdakul","year":"2020","journal-title":"J. RISK MODEL Valid. 14"},{"key":"10.1016\/j.asoc.2026.115790_bib41","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1785\/BSSA0820010505","article-title":"A stable algorithm for regression analyses using the random effects model","volume":"82","author":"Abrahamson","year":"1992","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib42","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1785\/gssrl.68.1.154","article-title":"Empirical Near-Source Attenuation Relationships for Horizontal and Vertical Components of Peak Ground Acceleration, Peak Ground Velocity, and Pseudo-Absolute Acceleration Response Spectra","volume":"68","author":"Campbell","year":"1997","journal-title":"Seismol. Res. Lett."},{"key":"10.1016\/j.asoc.2026.115790_bib43","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1190\/1.1442837","article-title":"Gridding with continuous curvature splines in tension","volume":"55","author":"Smith","year":"1990","journal-title":"GEOPHYSICS"},{"key":"10.1016\/j.asoc.2026.115790_bib44","series-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.asoc.2026.115790_bib45","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1177\/87552930211056081","article-title":"NGA-Subduction research program","volume":"38","author":"Bozorgnia","year":"2022","journal-title":"Earthq. SPECTRA"},{"key":"10.1016\/j.asoc.2026.115790_bib46","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1193\/072113EQS209M","article-title":"NGA-West2 Research Project","volume":"30","author":"Bozorgnia","year":"2014","journal-title":"Earthq. SPECTRA"},{"key":"10.1016\/j.asoc.2026.115790_bib47","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1177\/87552930211015695","article-title":"PEER NGA-East database","volume":"37","author":"Goulet","year":"2021","journal-title":"Earthq. SPECTRA"},{"key":"10.1016\/j.asoc.2026.115790_bib48","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1193\/070913EQS197M","article-title":"NGA-West2 Database","volume":"30","author":"Ancheta","year":"2014","journal-title":"Earthq. SPECTRA"},{"key":"10.1016\/j.asoc.2026.115790_bib49","doi-asserted-by":"crossref","first-page":"XV","DOI":"10.1186\/BF03353076","article-title":"Recent progress of seismic observation networks in Japan - Hi-net, F-net, K-NET and KiK-net","volume":"56","author":"Okada","year":"2004","journal-title":"EARTH PLANETS SPACE"},{"key":"10.1016\/j.asoc.2026.115790_bib50","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s10518-013-9506-8","article-title":"Reference database for seismic ground-motion in Europe (RESORCE)","volume":"12","author":"Akkar","year":"2014","journal-title":"Bull. Earthq. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib51","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.1785\/0120110271","article-title":"On the testing of ground-motion prediction equations against small-magnitude data","volume":"102","author":"Beauval","year":"2012","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib52","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1785\/0220180200","article-title":"Ground motions from induced earthquakes in Oklahoma and Kansas","volume":"90","author":"Moschetti","year":"2019","journal-title":"Seismol. Res. Lett."},{"key":"10.1016\/j.asoc.2026.115790_bib53","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1785\/0120200171","article-title":"Selecting ground-motion models for site-specific psha: Adaptability versus applicability","volume":"110","author":"Bommer","year":"2020","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib54","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1785\/0120200095","article-title":"Testing ground-motion prediction equations against moderate magnitude earthquake data recorded in Korea","volume":"111","author":"Nizamani","year":"2021","journal-title":"Bull. Seismol. Soc. Am."},{"key":"10.1016\/j.asoc.2026.115790_bib55","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1561\/2200000116","article-title":"Continual Learning as Computationally Constrained Reinforcement Learning","volume":"18","author":"Kumar","year":"2025","journal-title":"Found. Trends Mach. Learn."},{"key":"10.1016\/j.asoc.2026.115790_bib56","series-title":"Procedia Comput. Sci.","first-page":"340","article-title":"Mitigating catastrophic forgetting in medical imaging via incremental learning","author":"Leonard","year":"2025"},{"key":"10.1016\/j.asoc.2026.115790_bib57","series-title":"Procedia Comput. Sci.","first-page":"53","article-title":"Sleep, Neuromodulation, and Avoiding Forgetting","author":"Roberts","year":"2025"},{"key":"10.1016\/j.asoc.2026.115790_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.soildyn.2024.109100","article-title":"Physics-guided symbolic neural network reveals optimal functional forms describing ground motions","volume":"188","author":"Liu","year":"2025","journal-title":"Soil. Dyn. Earthqu. Eng."},{"key":"10.1016\/j.asoc.2026.115790_bib59","doi-asserted-by":"crossref","first-page":"7147","DOI":"10.1007\/s10518-025-02307-6","article-title":"Advancing ground-motion modeling through data fusion? Insights combining NGA-West2 data and CyberShake simulations","volume":"23","author":"Liu","year":"2025","journal-title":"Bull. Earthq. Engin"},{"key":"10.1016\/j.asoc.2026.115790_bib60","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"785","article-title":"XGBoost: A Scalable Tree Boosting System","author":"Chen","year":"2016"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462601238X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S156849462601238X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:45:06Z","timestamp":1782485106000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S156849462601238X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":60,"alternative-id":["S156849462601238X"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115790","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A near-real-time adaptive framework for ground motion prediction using incremental gradient boosting","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115790","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"115790"}}