A multi-task learning model for global soil moisture prediction based on adaptive weight allocation
Abstract Global soil moisture is crucial for multiple disciplines such as earth science and agricultural science. Therefore, there are many studies on how to improve the prediction accuracy of soil moisture, especially the rapid development of deep learning in recent years, which has greatly improve...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01894-3 |
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| author | Yeguang li Haiying Liu Tianshan Lv |
| author_facet | Yeguang li Haiying Liu Tianshan Lv |
| author_sort | Yeguang li |
| collection | DOAJ |
| description | Abstract Global soil moisture is crucial for multiple disciplines such as earth science and agricultural science. Therefore, there are many studies on how to improve the prediction accuracy of soil moisture, especially the rapid development of deep learning in recent years, which has greatly improved data-driven models. This paper proposes an adaptive weight long short-term memory (AW-LSTM) model based on dynamic weight allocation, which dynamically optimizes the model by calculating the correlation coefficient $$p$$ between tasks. The experimental verification demonstrated that the AW-LSTM model exhibited the most accurate soil moisture prediction, with $$R$$ and $$R^2$$ values of 0.9456 and 0.8489, respectively. These values were 0.019 and 0.072 higher than the single-task prediction values observed in the benchmark dataset. |
| format | Article |
| id | doaj-art-190d88fcb2b546e681dce45874790449 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-190d88fcb2b546e681dce458747904492025-08-20T01:59:56ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01894-3A multi-task learning model for global soil moisture prediction based on adaptive weight allocationYeguang li0Haiying Liu1Tianshan Lv2School of Economics and Management, Changchun University of TechnologySchool of Economics and Management, Changchun University of TechnologySchool of Economics and Management, Changchun University of TechnologyAbstract Global soil moisture is crucial for multiple disciplines such as earth science and agricultural science. Therefore, there are many studies on how to improve the prediction accuracy of soil moisture, especially the rapid development of deep learning in recent years, which has greatly improved data-driven models. This paper proposes an adaptive weight long short-term memory (AW-LSTM) model based on dynamic weight allocation, which dynamically optimizes the model by calculating the correlation coefficient $$p$$ between tasks. The experimental verification demonstrated that the AW-LSTM model exhibited the most accurate soil moisture prediction, with $$R$$ and $$R^2$$ values of 0.9456 and 0.8489, respectively. These values were 0.019 and 0.072 higher than the single-task prediction values observed in the benchmark dataset.https://doi.org/10.1038/s41598-025-01894-3Multi-task learningSoil moistureDeep learning |
| spellingShingle | Yeguang li Haiying Liu Tianshan Lv A multi-task learning model for global soil moisture prediction based on adaptive weight allocation Scientific Reports Multi-task learning Soil moisture Deep learning |
| title | A multi-task learning model for global soil moisture prediction based on adaptive weight allocation |
| title_full | A multi-task learning model for global soil moisture prediction based on adaptive weight allocation |
| title_fullStr | A multi-task learning model for global soil moisture prediction based on adaptive weight allocation |
| title_full_unstemmed | A multi-task learning model for global soil moisture prediction based on adaptive weight allocation |
| title_short | A multi-task learning model for global soil moisture prediction based on adaptive weight allocation |
| title_sort | multi task learning model for global soil moisture prediction based on adaptive weight allocation |
| topic | Multi-task learning Soil moisture Deep learning |
| url | https://doi.org/10.1038/s41598-025-01894-3 |
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