Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model
Abstract The stability of artificial sand-binding vegetation determines the success or failure of restoration of degraded ecosystem, accurately evaluating the stability of artificial sand-binding vegetation can provide evidence for the future management and maintenance of re-vegetated regions. In th...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33879-5 |
| _version_ | 1797841100673646592 |
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| author | Tonglin Fu Xinrong Li |
| author_facet | Tonglin Fu Xinrong Li |
| author_sort | Tonglin Fu |
| collection | DOAJ |
| description | Abstract The stability of artificial sand-binding vegetation determines the success or failure of restoration of degraded ecosystem, accurately evaluating the stability of artificial sand-binding vegetation can provide evidence for the future management and maintenance of re-vegetated regions. In this paper, a novel data-driven evaluation model was proposed by combining statistical methods and a neural network model to evaluate the stability of artificial sand-binding vegetation in the southeastern margins of the Tengger Desert, where the evaluation indexes were selected from vegetation, soil moisture, and soil. The evaluation results indicate that the stability of the artificially re-vegetated belt established in different years (1956a, 1964a, 1981a, and 1987a) tend to be stable with the increase of sand fixation years, and the artificially re-vegetated belts established in 1956a and 1964a have almost the same stability, but the stability of the artificially re-vegetated belt established in 1981a and 1987a have a significant difference. The evaluation results are reliable and accurate, which can provide evidence for the future management of artificial sand-binding vegetation. |
| first_indexed | 2024-04-09T16:25:34Z |
| format | Article |
| id | doaj.art-bf80349781ea4fe591a25841957ba1af |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T16:25:34Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-bf80349781ea4fe591a25841957ba1af2023-04-23T11:14:34ZengNature PortfolioScientific Reports2045-23222023-04-0113111010.1038/s41598-023-33879-5Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network modelTonglin Fu0Xinrong Li1School of Mathematics and Statistics, Longdong UniversityShapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of SciencesAbstract The stability of artificial sand-binding vegetation determines the success or failure of restoration of degraded ecosystem, accurately evaluating the stability of artificial sand-binding vegetation can provide evidence for the future management and maintenance of re-vegetated regions. In this paper, a novel data-driven evaluation model was proposed by combining statistical methods and a neural network model to evaluate the stability of artificial sand-binding vegetation in the southeastern margins of the Tengger Desert, where the evaluation indexes were selected from vegetation, soil moisture, and soil. The evaluation results indicate that the stability of the artificially re-vegetated belt established in different years (1956a, 1964a, 1981a, and 1987a) tend to be stable with the increase of sand fixation years, and the artificially re-vegetated belts established in 1956a and 1964a have almost the same stability, but the stability of the artificially re-vegetated belt established in 1981a and 1987a have a significant difference. The evaluation results are reliable and accurate, which can provide evidence for the future management of artificial sand-binding vegetation.https://doi.org/10.1038/s41598-023-33879-5 |
| spellingShingle | Tonglin Fu Xinrong Li Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model Scientific Reports |
| title | Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model |
| title_full | Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model |
| title_fullStr | Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model |
| title_full_unstemmed | Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model |
| title_short | Evaluating the stability of artificial sand-binding vegetation by combining statistical methods and a neural network model |
| title_sort | evaluating the stability of artificial sand binding vegetation by combining statistical methods and a neural network model |
| url | https://doi.org/10.1038/s41598-023-33879-5 |
| work_keys_str_mv | AT tonglinfu evaluatingthestabilityofartificialsandbindingvegetationbycombiningstatisticalmethodsandaneuralnetworkmodel AT xinrongli evaluatingthestabilityofartificialsandbindingvegetationbycombiningstatisticalmethodsandaneuralnetworkmodel |