A New Embedded Estimation Model for Soil Temperature Prediction

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.

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction

“…Underfitting refers to a model that can neither model the training data nor the testing data. The sweet spot between underfitting and overfitting, which shows the good performance of a machine learning algorithm on both training and testing data, is a good fit [5,27].…”

“…Multi-Layer Perceptron (MLP), a class of feedforward ANN, is a non-linear function approximator in layers using back propagation with no activation function in the output layer. It used rectified linear unit function as the activation function in the hidden layers [7,8,[26][27][28][29]:…”

“…The word "deep" is referring to the depth of layers in a neural network. A neural network that consists of more than three layers, which would be inclusive of the inputs and the output, can be considered a deep learning algorithm [17,27].…”

A Comprehensive Study of Artificial Intelligence Applications for Soil Temperature Prediction in Ordinary Climate Conditions and Extremely Hot Events

“…Multi-layer perceptron (MLP), a class of feedforward ANN, is a non-linear function approximator in layers using back propagation with no activation function in the output layer. It used the rectified linear unit (Relu) function as the activation function in the hidden layers [7,8,[36][37][38][39]:…”

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.…”