Gravity Predictions in Data-Missing Areas Using Machine Learning Methods
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 economi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4173 |
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| author | Yubin Liu Yi Zhang Qipei Pang Sulan Liu Shaobo Li Xuguo Shi Shaofeng Bian Yunlong Wu |
| author_facet | Yubin Liu Yi Zhang Qipei Pang Sulan Liu Shaobo Li Xuguo Shi Shaofeng Bian Yunlong Wu |
| author_sort | Yubin Liu |
| collection | DOAJ |
| description | 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—random forest, support vector machine, and recurrent neural network—and 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. |
| format | Article |
| id | doaj-art-6b6fb15635ab4551b64c0ef2e5b1d0a8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-6b6fb15635ab4551b64c0ef2e5b1d0a82024-11-26T18:19:50ZengMDPI AGRemote Sensing2072-42922024-11-011622417310.3390/rs16224173Gravity Predictions in Data-Missing Areas Using Machine Learning MethodsYubin Liu0Yi Zhang1Qipei Pang2Sulan Liu3Shaobo Li4Xuguo Shi5Shaofeng Bian6Yunlong Wu7Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaKey Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaKey Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, ChinaGravity 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—random forest, support vector machine, and recurrent neural network—and 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.https://www.mdpi.com/2072-4292/16/22/4173gravity data predictionmachine learningrandom forestrecurrent neural networksupport vector machine |
| spellingShingle | Yubin Liu Yi Zhang Qipei Pang Sulan Liu Shaobo Li Xuguo Shi Shaofeng Bian Yunlong Wu Gravity Predictions in Data-Missing Areas Using Machine Learning Methods Remote Sensing gravity data prediction machine learning random forest recurrent neural network support vector machine |
| title | Gravity Predictions in Data-Missing Areas Using Machine Learning Methods |
| title_full | Gravity Predictions in Data-Missing Areas Using Machine Learning Methods |
| title_fullStr | Gravity Predictions in Data-Missing Areas Using Machine Learning Methods |
| title_full_unstemmed | Gravity Predictions in Data-Missing Areas Using Machine Learning Methods |
| title_short | Gravity Predictions in Data-Missing Areas Using Machine Learning Methods |
| title_sort | gravity predictions in data missing areas using machine learning methods |
| topic | gravity data prediction machine learning random forest recurrent neural network support vector machine |
| url | https://www.mdpi.com/2072-4292/16/22/4173 |
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