{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:36Z","timestamp":1753881576680,"version":"3.41.2"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":147,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012243","name":"Foundation for Distinguished Young Talents in Higher Education of Guangdong","doi-asserted-by":"publisher","award":["2019KQNCX126"],"award-info":[{"award-number":["2019KQNCX126"]}],"id":[{"id":"10.13039\/501100012243","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M682883"],"award-info":[{"award-number":["2020M682883"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Sensors"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>A variety of climate factors influence the precision of the long\u2010term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long\u2010term GNSS monitoring records, this study combines the extended seven\u2010parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local\u2010scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long\u2010term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4\u2009mm\/year and 0.5\u2009mm\/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long\u2010term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long\u2010term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit\u2010less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS\u2010derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long\u2010term GNSS monitoring.<\/jats:p>","DOI":"10.1155\/2021\/9926442","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T03:36:52Z","timestamp":1622259412000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analyzing the Impact of Climate Factors on GNSS\u2010Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-5289","authenticated-orcid":false,"given":"Hanlin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4388-9925","authenticated-orcid":false,"given":"Linqiang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2574-0174","authenticated-orcid":false,"given":"Linchao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9453(2008)134:4(95)"},{"key":"e_1_2_9_2_2","unstructured":"UNAVCO (University NAVSTAR Consortium) Monumentation types 2014 http:\/\/www.unavco.org\/instrumentation\/monumentation\/types\/types.html."},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1029\/jb094ib08p10187"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10291-017-0609-6"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1029\/2001jb000573"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1029\/2009gl038152"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00190-013-0613-8"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/2016RG000529"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asr.2020.04.015"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-016-2344-7"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00190-016-0969-7"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00190-018-1167-6"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00190-010-0371-9"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00190-011-0444-4"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/2016JB013098"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jog.2013.08.004"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1029\/2001jb000570"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs11060680"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10291-012-0255-y"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10291-017-0682-x"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aau0323"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41561-018-0274-6"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1029\/2019gc008747"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018GL077049"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1029\/2019GL082706"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9453(2004)130:2(49)"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"ChenT.andGuestrinC. 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