Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data
Atmospheric data forecasting traditionally relies on physical models, which simulate atmospheric motion and change by solving atmospheric dynamics, thermodynamics, and radiative transfer processes. However, numerical models often involve significant computational demands and time constraints. In thi...
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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/2072-4292/16/22/4308 |
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| author | Yi Yan Linjing Guo Jiangting Li Zhouxiang Yu Shuji Sun Tong Xu Haisheng Zhao Lixin Guo |
| author_facet | Yi Yan Linjing Guo Jiangting Li Zhouxiang Yu Shuji Sun Tong Xu Haisheng Zhao Lixin Guo |
| author_sort | Yi Yan |
| collection | DOAJ |
| description | Atmospheric data forecasting traditionally relies on physical models, which simulate atmospheric motion and change by solving atmospheric dynamics, thermodynamics, and radiative transfer processes. However, numerical models often involve significant computational demands and time constraints. In this study, we analyze the performance of Gated Recurrent Units (GRU) and Long Short-Term Memory networks (LSTM) using over two decades of sounding data from the Xisha Island Observatory in the South China Sea. We propose a hybrid model that combines GRU and Random Forest (RF) in series, which predicts the presence of atmospheric ducts from limited data. The results demonstrate that GRU achieves prediction accuracy comparable to LSTM with 10% to 20% shorter running times. The prediction accuracy of the GRU-RF model reaches 0.92. This model effectively predicts the presence of atmospheric ducts in certain height regions, even with low data accuracy or missing data, highlighting its potential for improving efficiency in atmospheric forecasting. |
| format | Article |
| id | doaj-art-bfdc677fe53c49c0b103cd07b83b9e13 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-bfdc677fe53c49c0b103cd07b83b9e132025-08-20T01:53:56ZengMDPI AGRemote Sensing2072-42922024-11-011622430810.3390/rs16224308Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding DataYi Yan0Linjing Guo1Jiangting Li2Zhouxiang Yu3Shuji Sun4Tong Xu5Haisheng Zhao6Lixin Guo7School of Physics, Xidian University, Xi’an 710071, ChinaSchool of Physics, Xidian University, Xi’an 710071, ChinaSchool of Physics, Xidian University, Xi’an 710071, ChinaSchool of Physics, Xidian University, Xi’an 710071, ChinaChina Institute of Radio Wave Propagation, Qingdao 266107, ChinaChina Institute of Radio Wave Propagation, Qingdao 266107, ChinaChina Institute of Radio Wave Propagation, Qingdao 266107, ChinaSchool of Physics, Xidian University, Xi’an 710071, ChinaAtmospheric data forecasting traditionally relies on physical models, which simulate atmospheric motion and change by solving atmospheric dynamics, thermodynamics, and radiative transfer processes. However, numerical models often involve significant computational demands and time constraints. In this study, we analyze the performance of Gated Recurrent Units (GRU) and Long Short-Term Memory networks (LSTM) using over two decades of sounding data from the Xisha Island Observatory in the South China Sea. We propose a hybrid model that combines GRU and Random Forest (RF) in series, which predicts the presence of atmospheric ducts from limited data. The results demonstrate that GRU achieves prediction accuracy comparable to LSTM with 10% to 20% shorter running times. The prediction accuracy of the GRU-RF model reaches 0.92. This model effectively predicts the presence of atmospheric ducts in certain height regions, even with low data accuracy or missing data, highlighting its potential for improving efficiency in atmospheric forecasting.https://www.mdpi.com/2072-4292/16/22/4308atmospheric data forecastingGRULSTMRFatmospheric duct prediction |
| spellingShingle | Yi Yan Linjing Guo Jiangting Li Zhouxiang Yu Shuji Sun Tong Xu Haisheng Zhao Lixin Guo Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data Remote Sensing atmospheric data forecasting GRU LSTM RF atmospheric duct prediction |
| title | Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data |
| title_full | Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data |
| title_fullStr | Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data |
| title_full_unstemmed | Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data |
| title_short | Hybrid GRU–Random Forest Model for Accurate Atmospheric Duct Detection with Incomplete Sounding Data |
| title_sort | hybrid gru random forest model for accurate atmospheric duct detection with incomplete sounding data |
| topic | atmospheric data forecasting GRU LSTM RF atmospheric duct prediction |
| url | https://www.mdpi.com/2072-4292/16/22/4308 |
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