# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
###### Abstract  
Temperature is an important part of meteorological factors, which are affected by local and surrounding meteorological factors. Aiming at the problems of significant prediction error and insufficient extraction of spatial features in current temperature prediction research, this research proposes a temperature prediction model based on the Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) and studies the influence of temperature time-series characteristics, urban spatial location, and other meteorological factors on temperature change in the study area. In this research, multi-meteorological influencing factors and temperature time-series characteristics are used instead of single time-series temperature as influencing factors to improve the time dimension of the input data through time-sliding windows. Meanwhile, considering the influence of meteorological factors in the surrounding area on the temperature change in the study area, we use GCN to extract the urban geospatial location features. The experimental results demonstrate that our model outperforms other models and has the smallest root mean squared error (RMSE) and mean absolute error (MAE) in the following 14-day and multi-region temperature forecasts. It has higher accuracy in areas with stable temperature fluctuations and small temperature differences than in baseline models. temperature prediction; sequential features; geospatial location; GCN; BiLSTM 202220  
temperature data to obtain the prediction formula. For example, [PERSON] et al. [6] used the Kalman filter to correct temperature prediction errors, which improved the accuracy of short-term temperature prediction. However, mathematical statistics methods only analyze temperature factors, ignoring the impact of other meteorological factors on the results. Temperature is often affected by other meteorological factors, such as precipitation and atmospheric pressure. It is necessary to take multiple factors into account when making temperature predictions, where there is some correlation between the factors. Compared to mathematical statistics methods, machine learning-based approaches are able to explore the connections between temperature and other meteorological factors with higher accuracy. For example, [PERSON] et al. [7] used a support vector machine (SVM) with different kernel functions to predict the temperature with an average mean square error (MSE) of 0.09 \({}^{\circ}\)C. However, temperature data are a kind of time series data with prominent non-stationary fluctuation characteristics. These methods are good at exploring connections between different data but lack analysis of the time dependence of temperature data. With the rapid development of deep learning (DL) methods, deep neural networks have received increasing attention because they capture dynamic features well. Among them, prediction methods based on the Long Short-Term Memory (LSTM) model achieved relatively high accuracy [8], which can learn the information of timing features in the temperature data very well. However, LSTM only focuses on past temporal features and cannot completely learn the overall features of timing. Moreover, in addition to temporal dependence, spatial dependence also has a significant impact on temperature prediction. It is not enough to consider the meteorological factors and temporal dependence of the target area because the meteorological conditions in the surrounding area also have an impact on the target area. To address the above issues, in this research, we consider the relationships between temperature and various meteorological factors, and the spatial-temporal features of meteorological data. We combine Bidirectional Long Short-Term Memory (BiLSTM) [6] containing historical and future time-series characteristics with a Graph Convolutional Network (GCN), which can extract spatial meteorological characteristics to construct a GCN-BiLSTM temperature prediction model. The main contributions of this research are as follows:  
1.