Machine learning brings new insights for reducing salinization disaster
This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO2 concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (R2, root-mean-square error, RPD) va...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1130070/full |
| _version_ | 1828038360627675136 |
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| author | Peng An Wenfeng Wang Wenfeng Wang Wenfeng Wang Xi Chen Xi Chen Xi Chen Xi Chen Zhikai Zhuang Lujie Cui |
| author_facet | Peng An Wenfeng Wang Wenfeng Wang Wenfeng Wang Xi Chen Xi Chen Xi Chen Xi Chen Zhikai Zhuang Lujie Cui |
| author_sort | Peng An |
| collection | DOAJ |
| description | This study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO2 concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (R2, root-mean-square error, RPD) values at the training stage, validation stage and testing stage are (0.9924, 0.0123, and 8.282), (0.9931, 0.0872, and 7.0918), (0.9826, 0.1079, and 6.0418), respectively. Based on the underlining mechanisms, we conjecture that subterranean CO2 sequestration could reduce salinization disaster in deserts. |
| first_indexed | 2024-04-10T16:24:37Z |
| format | Article |
| id | doaj.art-736e004a41de464db363fc4b1d7de2cc |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-04-10T16:24:37Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-736e004a41de464db363fc4b1d7de2cc2023-02-09T07:23:40ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-02-011110.3389/feart.2023.11300701130070Machine learning brings new insights for reducing salinization disasterPeng An0Wenfeng Wang1Wenfeng Wang2Wenfeng Wang3Xi Chen4Xi Chen5Xi Chen6Xi Chen7Zhikai Zhuang8Lujie Cui9Ningbo University of Technology, Ningbo, ChinaNingbo University of Technology, Ningbo, ChinaShanghai Institute of Technology, Shanghai, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaXinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, ChinaUniversity of Chinese Academy of Sciences, Beijing, ChinaSino-Belgian Joint Laboratory of Geo-Information, Urumqi, ChinaCAS Research Centre for Ecology and Environment of Central Asia, Urumqi, ChinaThe University of Hong Kong, Hong Kong, ChinaShanghai Institute of Technology, Shanghai, ChinaThis study constructs a machine learning system to examine the predictors of soil salinity in deserts. We conclude that soil humidity and subterranean CO2 concentration are two leading controls of soil salinity—respectively explain 71.33%, 13.83% in the data. The (R2, root-mean-square error, RPD) values at the training stage, validation stage and testing stage are (0.9924, 0.0123, and 8.282), (0.9931, 0.0872, and 7.0918), (0.9826, 0.1079, and 6.0418), respectively. Based on the underlining mechanisms, we conjecture that subterranean CO2 sequestration could reduce salinization disaster in deserts.https://www.frontiersin.org/articles/10.3389/feart.2023.1130070/fullsalinization disasterprincipal components analysis (PCA)artificial neural network (ANN)long short-term memory (LSTM)subterranean CO2 sequestration |
| spellingShingle | Peng An Wenfeng Wang Wenfeng Wang Wenfeng Wang Xi Chen Xi Chen Xi Chen Xi Chen Zhikai Zhuang Lujie Cui Machine learning brings new insights for reducing salinization disaster Frontiers in Earth Science salinization disaster principal components analysis (PCA) artificial neural network (ANN) long short-term memory (LSTM) subterranean CO2 sequestration |
| title | Machine learning brings new insights for reducing salinization disaster |
| title_full | Machine learning brings new insights for reducing salinization disaster |
| title_fullStr | Machine learning brings new insights for reducing salinization disaster |
| title_full_unstemmed | Machine learning brings new insights for reducing salinization disaster |
| title_short | Machine learning brings new insights for reducing salinization disaster |
| title_sort | machine learning brings new insights for reducing salinization disaster |
| topic | salinization disaster principal components analysis (PCA) artificial neural network (ANN) long short-term memory (LSTM) subterranean CO2 sequestration |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1130070/full |
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