Temperature is one of the most important meteorological elements, which affects the daily lives of people all over the world. Owing to the rapid development of meteorological facilities, the number of meteorological observation stations on earth is gradually increasing, which brings challenges to the spatial association between stations. Many researchers focus on how to predict temperature more accurately utilizing these associations. However, the existing deep learning methods of temperature prediction have difficulty in capturing the interactions between neighboring stations in the spatial dimension. In addition, in the time dimension, the temperature in nature exhibits not only nearby variations but also periodic characteristics, which further increases the difficulty of temperature prediction. To solve the aforementioned two problems, we propose the periodicity and closeness social long short-term memory (PCSLSTM) model, which includes PS-LSTM and CS-LSTM modules. Specifically, to model the relationships between multiple meteorological observation stations, we utilized the social pooling in the PS-LSTM and CS-LSTM modules to establish spatial associations. To further refine the temperature variation, we combine PS-LSTM and CS-LSTM to model the periodicity and closeness of the time series. Compared with the LSTM basic model, the experiments show that the MAE of our model prediction results is reduced by 0.109 degrees C in the next 24 h compared. (c) 2022 Elsevier B.V. All rights reserved.
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