Abstract
With the continuous development of Earth science, soil temperature has received more and more attention in Earth system research as an important parameter. The change of soil temperature (Ts) in different regions and related time series is affected by many factors, which bring certain difficulties to the accuracy of soil temperature prediction and the robustness of the algorithm. In this paper, an embedded network prediction model based on the gated recurrent unit (GRU) model is proposed to learn the local and global features of historical temperature for improving the prediction performance of soil temperature. We input different steps into the GRU model, and the output is weighted to obtain the final prediction result. In order to obtain the global characteristics of soil temperature, we connect the previous steps to the output layer directly, and the local characteristics of soil temperature are obtained through the following steps. This paper uses the soil temperature data from two meteorological stations (Laegern and Fluehli) in Switzerland as the input data to predict the soil temperature for different soil depths (5 cm, 10 cm, and 15 cm) at different time points (6 hrs, 12 hrs, and 24 hrs), using RMSE, MAE, MSE, and R 2 performance indicators as evaluation criteria to verify the accuracy of prediction. As the experimental results show, our method has the best performance compared to the others (artificial neural networks (ANN), extreme learning machine model (ELM), long short-term memory network (LSTM), gated recurrent unit network (GRU)). In particular, we estimated the soil temperature at the soil depth of 10 cm of the Fluehli station in the coming 6 hrs; our method achieved the best performance; and, meanwhile, our model achieved the maximum value of R 2 (0.9914) and the minimum values of RMSE (0.4668), MAE (0.2585), and MSE (0.2214) compared with the other four models. Therefore, our model can not only predict the soil temperature at different depths but also improve the accuracy.
Highlights
Geoscience has played an important role in social development and economic construction; soil temperature (Ts) and its daily fluctuations are among the most vital meteorological parameters in Earth sciences, such as agriculture, forestry, and geology [1, 2]; and it is an important variable of land-atmosphere interactions [3]
The data collected in some regions is unavailable that cannot be used to predict, which will reduce the accuracy of model predictions [7]. erefore, this paper recommends using time series as the input data for the soil temperature prediction model
In order to obtain the global characteristics of soil temperature, we connect the previous steps to the output layer directly, and the local characteristics of soil temperature are obtained through the following steps
Summary
Geoscience has played an important role in social development and economic construction; soil temperature (Ts) and its daily fluctuations are among the most vital meteorological parameters in Earth sciences, such as agriculture, forestry, and geology [1, 2]; and it is an important variable of land-atmosphere interactions [3]. Erefore, the accurate prediction of soil temperature at different depths can be used to guide practical applications in some fields, which is instead of using traditional sensors manually for on-site measurement [5]. Researchers usually use methods based on physical models to predict soil temperature through the heat transfer mechanism of the soil itself mainly [6, 8]. Ghorbani et al proposed a method based on the support vector machine to estimate the soil field capacity and permanent wilting point [13]. It plays an important role in the soil temperature field [14]. LSTM has received attention from researchers [16]
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