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Research on soil moisture prediction model based on deep learning
Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is diffic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447191/ https://www.ncbi.nlm.nih.gov/pubmed/30943228 http://dx.doi.org/10.1371/journal.pone.0214508 |
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author | Cai, Yu Zheng, Wengang Zhang, Xin Zhangzhong, Lili Xue, Xuzhang |
author_facet | Cai, Yu Zheng, Wengang Zhang, Xin Zhangzhong, Lili Xue, Xuzhang |
author_sort | Cai, Yu |
collection | PubMed |
description | Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control. |
format | Online Article Text |
id | pubmed-6447191 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-64471912019-04-17 Research on soil moisture prediction model based on deep learning Cai, Yu Zheng, Wengang Zhang, Xin Zhangzhong, Lili Xue, Xuzhang PLoS One Research Article Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control. Public Library of Science 2019-04-03 /pmc/articles/PMC6447191/ /pubmed/30943228 http://dx.doi.org/10.1371/journal.pone.0214508 Text en © 2019 Cai et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cai, Yu Zheng, Wengang Zhang, Xin Zhangzhong, Lili Xue, Xuzhang Research on soil moisture prediction model based on deep learning |
title | Research on soil moisture prediction model based on deep learning |
title_full | Research on soil moisture prediction model based on deep learning |
title_fullStr | Research on soil moisture prediction model based on deep learning |
title_full_unstemmed | Research on soil moisture prediction model based on deep learning |
title_short | Research on soil moisture prediction model based on deep learning |
title_sort | research on soil moisture prediction model based on deep learning |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447191/ https://www.ncbi.nlm.nih.gov/pubmed/30943228 http://dx.doi.org/10.1371/journal.pone.0214508 |
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