Deep Spatio-Temporal Attention Model for Grain Storage Temperature Forecasting

Shanshan Duan, Weidong Yang, Xuyu Wang, Shiwen Mao, Yuan Zhang

Abstract: Temperature is one of the major ecological factors that affect the safe storage of grain. In this paper, we propose a deep spatio-temporal attention mode to predict stored grain temperature, which exploits the historical temperature data of stored grain and the meteorological data of the region. In this proposed model, we use the Sobel operator to extract the local spatial factors, and leverage the attention mechanism to obtain the global spatial factors of grain temperature data and temporal information. In addition, a convolutional neural network (CNN) is used to learn features of external meteorological factors. Finally, the spatial factors of grain pile and external meteorological factors are combined to predict future grain temperature using long short-term memory (LSTM) based encoder and decoder models. Experiment results show that the proposed model achieves higher predication accuracy compared with the traditional methods.