气候变化领域集成服务门户(CCPortal): Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau

CCPortal

DOI	10.7717/peerj.15851
	Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau
	Zhao, Lufei; Luo, Tonglin; Jiang, Xuchu; Zhang, Biao
发表日期	2023
ISSN	2167-8359
卷号	11
英文摘要	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.
关键词	Soil moisture Time series prediction STL decomposition BiGRU LSTM Qinghai-Tibet Plateau
WOS研究方向	Multidisciplinary Sciences
WOS记录号	WOS:001109706100002
来源期刊	PEERJ
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/283062
作者单位	Liaocheng University; Zhongnan University of Economics & Law; Liaocheng University
推荐引用方式 GB/T 7714	Zhao, Lufei,Luo, Tonglin,Jiang, Xuchu,et al. Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau[J],2023,11.
APA	Zhao, Lufei,Luo, Tonglin,Jiang, Xuchu,&Zhang, Biao.(2023).Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau.PEERJ,11.
MLA	Zhao, Lufei,et al."Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai-Tibet Plateau".PEERJ 11(2023).