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Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau

Ali Network data based on the Qinghai-Tibetan Plateau (QTP) can provide representative coverage of the climate and surface hydrometeorological conditions in the cold and arid region of the QTP. Among them, the plateau soil moisture can effectively quantify the uncertainty of coarse resolution satell...

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Autores principales: Zhao, Lufei, Luo, Tonglin, Jiang, Xuchu, Zhang, Biao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448883/
https://www.ncbi.nlm.nih.gov/pubmed/37637158
http://dx.doi.org/10.7717/peerj.15851
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author Zhao, Lufei
Luo, Tonglin
Jiang, Xuchu
Zhang, Biao
author_facet Zhao, Lufei
Luo, Tonglin
Jiang, Xuchu
Zhang, Biao
author_sort Zhao, Lufei
collection PubMed
description Ali Network data based on the Qinghai-Tibetan Plateau (QTP) can provide representative coverage of the climate and surface hydrometeorological conditions in the cold and arid region of the QTP. Among them, the plateau soil moisture can effectively quantify the uncertainty of coarse resolution satellite and soil moisture models. With the objective of constructing an “end-to-end” soil moisture prediction model for the Tibetan Plateau, a combined prediction model based on time series decomposition and a deep neural network is proposed in this article. The model first performs data preprocessing and seasonal-trend decomposition using loess (STL) to obtain the trend component, seasonal component and random residual component of the original time series in an additive way. Subsequently, the bidirectional gated recurrent unit (BiGRU) is used for the trend component, and the long short-term memory (LSTM) is used for the seasonal and residual components to extract the time series information. The experiments based on the measured data demonstrate that the use of STL decomposition and the combination model can effectively extract the information in soil moisture series using its concise and clear structure. The proposed model in this article has a stable performance improvement of 5–30% over a single model and existing prediction models in different prediction time domains. In long-range prediction, the proposed model also achieves the best accuracy in the shape and temporal domains described by using dynamic time warping (DTW) index and temporal distortion index (TDI). In addition, the generalization performance experiments show that the combined method proposed in this article has strong reference value for time series prediction of natural complex systems.
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spelling pubmed-104488832023-08-25 Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau Zhao, Lufei Luo, Tonglin Jiang, Xuchu Zhang, Biao PeerJ Ecosystem Science Ali Network data based on the Qinghai-Tibetan Plateau (QTP) can provide representative coverage of the climate and surface hydrometeorological conditions in the cold and arid region of the QTP. Among them, the plateau soil moisture can effectively quantify the uncertainty of coarse resolution satellite and soil moisture models. With the objective of constructing an “end-to-end” soil moisture prediction model for the Tibetan Plateau, a combined prediction model based on time series decomposition and a deep neural network is proposed in this article. The model first performs data preprocessing and seasonal-trend decomposition using loess (STL) to obtain the trend component, seasonal component and random residual component of the original time series in an additive way. Subsequently, the bidirectional gated recurrent unit (BiGRU) is used for the trend component, and the long short-term memory (LSTM) is used for the seasonal and residual components to extract the time series information. The experiments based on the measured data demonstrate that the use of STL decomposition and the combination model can effectively extract the information in soil moisture series using its concise and clear structure. The proposed model in this article has a stable performance improvement of 5–30% over a single model and existing prediction models in different prediction time domains. In long-range prediction, the proposed model also achieves the best accuracy in the shape and temporal domains described by using dynamic time warping (DTW) index and temporal distortion index (TDI). In addition, the generalization performance experiments show that the combined method proposed in this article has strong reference value for time series prediction of natural complex systems. PeerJ Inc. 2023-08-21 /pmc/articles/PMC10448883/ /pubmed/37637158 http://dx.doi.org/10.7717/peerj.15851 Text en © 2023 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Ecosystem Science
Zhao, Lufei
Luo, Tonglin
Jiang, Xuchu
Zhang, Biao
Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title_full Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title_fullStr Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title_full_unstemmed Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title_short Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau
title_sort prediction of soil moisture using bigru-lstm model with stl decomposition in qinghai–tibet plateau
topic Ecosystem Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10448883/
https://www.ncbi.nlm.nih.gov/pubmed/37637158
http://dx.doi.org/10.7717/peerj.15851
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