{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T03:04:52Z","timestamp":1774062292410,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T00:00:00Z","timestamp":1731024000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Fund of China","award":["42274113"],"award-info":[{"award-number":["42274113"]}]},{"name":"the National Natural Science Fund of China","award":["42274111"],"award-info":[{"award-number":["42274111"]}]},{"name":"the National Natural Science Fund of China","award":["41931074"],"award-info":[{"award-number":["41931074"]}]},{"name":"the National Natural Science Fund of China","award":["42304049"],"award-info":[{"award-number":["42304049"]}]},{"name":"the National Natural Science Fund of China","award":["42430101"],"award-info":[{"award-number":["42430101"]}]},{"name":"the National Natural Science Fund of China","award":["2023AFB017"],"award-info":[{"award-number":["2023AFB017"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["42274113"],"award-info":[{"award-number":["42274113"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["42274111"],"award-info":[{"award-number":["42274111"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["41931074"],"award-info":[{"award-number":["41931074"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["42304049"],"award-info":[{"award-number":["42304049"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["42430101"],"award-info":[{"award-number":["42430101"]}]},{"name":"Hubei Provincial Natural Science Foundation of China","award":["2023AFB017"],"award-info":[{"award-number":["2023AFB017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Gravity data, comprising a key foundational dataset, are crucial for various research, including land subsidence monitoring, geological exploration, and navigational positioning. However, the collection of gravity data in specific regions is difficult because of environmental, technical, and economic constraints, resulting in a non-uniform distribution of the observational data. Traditionally, interpolation methods such as Kriging have been widely used to deal with data gaps; however, their predictive accuracy in regions with sparse data still needs improvement. In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms\u2014random forest, support vector machine, and recurrent neural network\u2014and compared their performances against the traditional Kriging interpolation method. The results indicate that machine learning methods exhibit a marked advantage in gravity data prediction, significantly enhancing the predictive accuracy.<\/jats:p>","DOI":"10.3390\/rs16224173","type":"journal-article","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T06:05:41Z","timestamp":1731045941000},"page":"4173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Gravity Predictions in Data-Missing Areas Using Machine Learning Methods"],"prefix":"10.3390","volume":"16","author":[{"given":"Yubin","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1006-6372","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Qipei","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3013-3190","authenticated-orcid":false,"given":"Sulan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2815-7897","authenticated-orcid":false,"given":"Xuguo","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Shaofeng","family":"Bian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5487-5078","authenticated-orcid":false,"given":"Yunlong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"ref_1","first-page":"342","article-title":"Comparison and analysis of high-precision gravity data gridding methods","volume":"35","author":"Sun","year":"2015","journal-title":"J. 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