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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...

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Autores principales: Cai, Yu, Zheng, Wengang, Zhang, Xin, Zhangzhong, Lili, Xue, Xuzhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
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.
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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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