# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 2 Related Works  
Traditional temperature prediction methods can be classified into two categories: mathematical statistics-based and machine learning-based. Common mathematical statistics methods include the Kalman filter [9] and regression analysis [10]. These methods are simple in principle and fast in calculation, but they only analyze a single variable and are highly dependent on current temperature data. They do not perform well in long-term temperature predictions. More importantly, the influence of other meteorological factors is not considered on temperature, and the prediction accuracy is not high. Machine learning-based methods are able to explore the correlation features of each meteorological factor to achieve excellent predictive performance. Common methods of machine learning include SVM [11; 12], Genetic Algorithms (GA) [13], Artificial Neural Networks (ANN) [14], etc. [PERSON] et al. [12] used SVM to predict atmospheric temperature and compared the results with multilayer perceptron (MLP). The results demonstrated that SVM outperformed MLP's. [PERSON] et al. [14] used the ANN model to predict temperature in six local regions with good performance, including Swiss Alps, Canadian Rockies, Tasmania, and so on. [PERSON] et al. [13] applied GA to determine the optimal duration and resolution of the input variables and set them as inputs for the ANN model. The improved method enhanced the MAE of temperature prediction for the next 1, 2, 8, and 12 h compared to the traditional GA method. In these methods, the current inputs are used to output the prediction results through mathematical functions or algorithms. These methods, based on machine learning, ignore the analysis of timing dependence. Temperature prediction is a typical time series problem that is highly dependent on time series. Therefore, their prediction accuracy cannot be further improved when faced with a long-term temperature prediction task. These traditional methods of temperature prediction are not good at handling large amounts of data. In recent years, DL methods have been widely used in the prediction of future weather, such as PM2.5, wind speed, rainfall, etc. In particular, it has better performance than other traditional methods when processing time series data using LSTM. [PERSON] et al. [15] leveraged the LSTM to analyze the multi-year surface meteorological data of the city and predicted the future temperature with a precision of 86%. [PERSON] et al. [8] used the LSTM method to predict soil temperature. Considering air quality data and meteorological data, [PERSON] et al. [16] proposed an improved LSTM method to predict PM2.5 concentrations using big data. However, the above research only analyzed the data of the target region without considering the influence factors of multiple regions. More and more researchers are focusing on the effects of spatial-temporal dependence in DL models. The meteorological change of a place is not only affected by its own geographical and temporal factors. Therefore, it is necessary to improve prediction accuracy by analyzing the observation data of the surrounding areas of the region. For example, [PERSON] et al. [17] used a 3-Dimensional Convolutional Neural Network (3D CNN) to capture the spatial correlations among Sea Surface Temperature (SST) field data composed of multiple observation points in a selected sea area for SST prediction. [PERSON] et al. [18] used BiLSTM to process time series observations and CNN to process RDASP image data to obtain spatial features, and combined both features to predict the temperature in the next 14 days. In other research fields, [PERSON] et al. [19] proposed an end-to-end model called ITRCN, which converts interactive network traffic into images. The model used CNN to capture the interaction function of traffic and GRU to extract temporal features. The results show that the ITRCN method outperforms the conventional GRU and CNN methods by an improvement of 14.3% and 13.0% in RMSE, respectively.