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Article

Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network

by 1,2,3, 1,3,* and 2
1
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2
College of Computer and Communication, LanZhou University of Technology, Lanzhou 730050, China
3
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(7), 173; https://doi.org/10.3390/a13070173
Received: 11 June 2020 / Revised: 12 July 2020 / Accepted: 15 July 2020 / Published: 17 July 2020
Soil temperature (ST) plays a key role in the processes and functions of almost all ecosystems, and is also an essential parameter for various applications such as agricultural production, geothermal development, and their utilization. Although numerous machine learning models have been used in the prediction of ST, and good results have been obtained, most of the current studies have focused on daily or monthly ST predictions, while hourly ST predictions are scarce. This paper presents a novel scheme for forecasting the hourly ST using weather forecast data. The method considers the hourly ST prediction to be the superposition of two parts, namely, the daily average ST prediction and the ST amplitude (the difference between the hourly ST and the daily average ST) prediction. According to the results of correlation analysis, we selected nine meteorological parameters and combined two temporal parameters as the input vectors for predicting the daily average ST. For the task of predicting the ST amplitude, seven meteorological parameters and one temporal parameter were selected as the inputs. Two submodels were constructed using a deep bidirectional long short-term memory network (BiLSTM). For the task of hourly ST prediction at five different soil depths at 30 sites, which are located in 5 common climates in the United States, the results showed the method proposed in this paper performs best at all depths for 30 stations (100% of all) for the root mean square error (RMSE), 27 stations (90% of all) for the mean absolute error (MAE), and 30 stations (100% of all) for the coefficient of determination (R2), respectively. Moreover, the method adopted in this study displays a stronger ST prediction ability than the traditional methods under all climate types involved in the experiment, the hourly ST produced by it can be used as a driving parameter for high-resolution biogeochemical models, land surface models and hydrological models and can provide ideas for an analysis of other time series data. View Full-Text
Keywords: soil temperature; machine learning; weather forecasting data; BiLSTM; soil depths soil temperature; machine learning; weather forecasting data; BiLSTM; soil depths
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MDPI and ACS Style

Li, C.; Zhang, Y.; Ren, X. Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network. Algorithms 2020, 13, 173. https://doi.org/10.3390/a13070173

AMA Style

Li C, Zhang Y, Ren X. Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network. Algorithms. 2020; 13(7):173. https://doi.org/10.3390/a13070173

Chicago/Turabian Style

Li, Cong, Yaonan Zhang, and Xupeng Ren. 2020. "Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network" Algorithms 13, no. 7: 173. https://doi.org/10.3390/a13070173

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