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The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China
The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ET(O) in the absence of meteorological data, this study use XGBoost to select key influencing fac...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191727/ https://www.ncbi.nlm.nih.gov/pubmed/35696403 http://dx.doi.org/10.1371/journal.pone.0269746 |
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author | Zhao, Zhenhua Feng, Guohua Zhang, Jing |
author_facet | Zhao, Zhenhua Feng, Guohua Zhang, Jing |
author_sort | Zhao, Zhenhua |
collection | PubMed |
description | The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ET(O) in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ET(O) prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that T(max), n and Ra can be used as the input combination of ET(O) model construction, and T(max) is the primary factor affecting ET(O). ET(O) model constructed by BP algorithm has good goodness of fit with the ET(O) calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R(2), MAE, NSE, GPI ranging 0.200–0.377, 0.932–0.984, 0.140–0.261, 0.920–0.984, 1.472–2.000, GPI ranking is 1–23. Therefore, the input combination (T(max), n and Ra) and CSO-BP model are recommended as a simplified model for ET(O) prediction in Southwest China. |
format | Online Article Text |
id | pubmed-9191727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91917272022-06-14 The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China Zhao, Zhenhua Feng, Guohua Zhang, Jing PLoS One Research Article The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ET(O) in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ET(O) prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that T(max), n and Ra can be used as the input combination of ET(O) model construction, and T(max) is the primary factor affecting ET(O). ET(O) model constructed by BP algorithm has good goodness of fit with the ET(O) calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R(2), MAE, NSE, GPI ranging 0.200–0.377, 0.932–0.984, 0.140–0.261, 0.920–0.984, 1.472–2.000, GPI ranking is 1–23. Therefore, the input combination (T(max), n and Ra) and CSO-BP model are recommended as a simplified model for ET(O) prediction in Southwest China. Public Library of Science 2022-06-13 /pmc/articles/PMC9191727/ /pubmed/35696403 http://dx.doi.org/10.1371/journal.pone.0269746 Text en © 2022 Zhao et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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 Zhao, Zhenhua Feng, Guohua Zhang, Jing The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title_full | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title_fullStr | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title_full_unstemmed | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title_short | The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China |
title_sort | simplified hybrid model based on bp to predict the reference crop evapotranspiration in southwest china |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9191727/ https://www.ncbi.nlm.nih.gov/pubmed/35696403 http://dx.doi.org/10.1371/journal.pone.0269746 |
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