Advanced hybrid empirical mode decomposition, convolutional neural network and long short-term memory neural network approach for predicting grain pile humidity based on meteorological inputs

Abstract

Grain pile humidity prediction is beneficial to ensure food security, and establishing an effective humidity prediction model is of great significance to the field of grain storage. By taking meteorological and grain temperature data as inputs, we propose a prediction model that combines Empirical Mode Decomposition (EMD), Convolutional Neural Network (CNN), and Long Short-Term Memory Network (LSTM). The model was verified in experimental data of three different storage layers of grain piles. The prediction results show that the proposed EMD-CNN-LSTM model has better prediction accuracy than the other three comparison models: CNN-LSTM, CNN and LSTM. From the average results of the entire granary, the MAE, RMSE, and MAPE results are 0.14, 0.18, and 0.25%, respectively, and the MAE value is 44% higher than the previous research method that does not consider meteorological factors. The MAE, RMSE, and MAPE results of the CNN-LSTM method with EMD decomposition were improved by 58%, 53% and 58% respectively compared with the method without EMD decomposition. It can be concluded that taking meteorological factors as model input and integrating EMD methods can improve prediction accuracy. The constructed prediction model shows effective prediction results in different storage layers of grain pile, which provides new insights for ensuring food security and also provides valuable references for multivariate time series prediction in other fields.