Soil Temperature Prediction Using Convolutional Neural Network Based on Ensemble Empirical Mode Decomposition

Soil temperature plays an important role in agriculture, industry and other fields. Accurate soil temperature prediction can help improve productivity and avoid risks in many fields. At present, many machine learning methods have been applied to soil temperature prediction such as support vector regression (SVR), artificial neural network (ANN), long short-term memory neural network (LSTM) and others. In this article, we propose a machine learning model called convolutional neural network based on ensemble empirical mode decomposition (EEMD-CNN) to predict soil temperature. In this model, ensemble empirical mode decomposition (EEMD) is used to decompose original soil temperature series into several intrinsic mode functions (IMFs). After decomposition, the original series are combined with IMFs to get new two-dimension input data as the input of the convolutional neural network (CNN). By comparing the results which is predicted by the trained model with the original soil temperature series and other four models of persistence forecast (PF), backpropagation neural network (BPNN), LSTM and EEMD-LSTM these, the result shows that EEMD-CNN has the better performance than other four models. EEMD-CNN shows good performance not only on predicting next day's soil temperature but also on predicting several days delay's temperature also has good performance. It is concluded that the proposed EEMD-CNN model in this study is a suitable tool for soil temperature prediction.