# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 3 Materials and Methods  
### Data Source and Preprocessing  
#### 3.1.1 Data Source  
The Daily Value data set of China's Surface Climate Data (V3.0) [21] for this research between 1 May 2010, and 31 December 2019, including 2071 primary meteorological stations on the Chinese mainland, was obtained from the National Meteorological Information Center and the China Meteorological Data Service Center, which were selected as the sample data set. Some of the original data from Heilongjiang Station (District Station No.: 50136) are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Advantages** & **Disadvantages** \\ \hline Mathematical statistics (e.g., ARIMA) & Simple principle; fast calculation speed. & Single factor analysis; poor long-term predictive performance. \\ \hline Machine learning methods (e.g., SVM) & Expertise in uncovering correlations between the factors. & Lack of analysis of time series. \\ \hline Time series methods (e.g., LSTM) & Simple modeling; analysis of time series features. & Lack of analysis of spatial feature; low prediction accuracy when there are few time samples. \\ \hline Spatial-temporal feature methods (e.g., CNN-BiLSTM) & Consider spatial-temporal characteristics; good at processing raster data and regular structure. & Poor performance in handling irregular graph structures \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of temperature prediction methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**District** & **Date** & **Temperature** & **Relative** & **Precipitation** & **Air Pressure** & **Wind Speed** \\
**Station** & & (\({}^{\circ}\)C) & **Humidity** & **(0.1 mm)** & **(0.1 hPa)** & & **(0.1 m/s)** \\ \hline
50136 & 1 May 2014 & 5.1 & 38 & 0 & 96,340 &... & 3.5 \\
50136 & 2 May 2014 & 1.3 & 36 & 0 & 96,610 &... & 1.4 \\
50136 & 3 May 2014 & 2.3 & 34 & 0 & 96,600 &... & 2.2 \\
50136 & 4 May 2014 & 2.7 & 33 & 0 & 96,660 &... & 3 \\
50136 & 5 May 2014 & 2.2 & 71 & 1.8 & 96,250 &... & 2.7 \\
50136 & 6 May 2014 & 4.9 & 75 & 0 & 96,090 &... & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Part of the original daily value data of Heilongjiang station of China’s surface climate data.  
The datasets include the station number, location, and other station information of 2071 stations, as well as daily updated values of 17 related meteorological element values. The data elements include station number, latitude, longitude, elevation, date, average temperature, maximum temperature, minimum temperature, average relative humidity, minimum relative humidity, average wind speed, maximum wind speed, sunshine duration, precipitation, small evaporation, large evaporation, average pressure, maximum pressure, minimum pressure, average surface temperature, maximum surface temperature, and minimum surface temperature. The units of the original data are described as follows: temperature (\({}^{\circ}\)C), humidity (1%), wind speed (0.1 m/s), sunshine (0.1 h), precipitation (0.1 mm), evaporation (0.1 mm), air pressure (0.1 hPa), latitude (d, m), longitude (d, m), and elevation (0.1 m).