Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network

Abstract

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.

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

“…Li et al [99] used a bidirectional LSTM model to estimate soil temperature at 30 sites under five different climate types. Soil temperature (ST) measurements were obtained from the U.S. Department of Agriculture's National Water and Climate Center, which has established more than 200 sites across the country to collect data on meteorology, soil, and solar radiation.…”

“…The type of input affects the performance of the model [55,62,88,92]. Removing background from images [55], using different color spaces and vegetarian indices as input [62], detecting crop at different growth stages [81], adding vegetarian indices to the input [30,86], cropping the input image [91], and data from different climate types [99] change the performance of the model. The size of the input also affects the runtime and accuracy of the model [62,79].…”

A Comprehensive Study Of Artificial Intelligence Applications For Soil Temperature Prediction

“…Li et al (2020) presented a novel scheme for forecasting the hourly soil temperature at five different soil depths [11]. They developed an integrated deep bidirectional long short-term memory network (BiLSTM) and fed their model with air temperature, wind speed, solar radiation, relative humidity, vapor pressure and dew point.…”

A Novel Machine Learning Approach: Soil Temperature Ordinal Classification (STOC)

“…More specifically, this study brings together notions from the fields of agriculture and machine learning for information fusion. In the regression studies, DT (Sattari et al 2020;Sanikhani et al 2018), Support Vector Regression (SVR) (Li et al 2020a;Li et al 2020b;Shamshirband et al 2020;Delbari et al 2019;Mehdizadeh et al 2018;Xing et al 2018), RF (Alizamir et al 2020b;Tsai et al 2020;Feng et al 2019), NN (Abimbola et al 2021;Bayatvarkeshi et al 2021;Wang et al 2021;Hao et al 2020;Penghui et al 2020;Citakoglu 2017;Abyaneh et al 2016;Kisi et al 2015), ELM (Alizamir et al 2020a) algorithms have been preferred for predicting soil temperatures. In addition, some of the time-series studies have also applied NN (Li et al 2020c;Bonakdari et al 2019), ELM (Zeynoddin et al 2020;Mehdizadeh et al 2020), SVR (Nanda et al 2020) algorithms for the prediction performance comparison.…”