# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 3 Materials and Methods
### Methods
#### 3.2.1 GCN Model
Nodes 2, 3, and 4 can be considered \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
2 & 1 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
3 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
4 & 1 & 0 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
5 & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 1 & 1 & 0 & 0 & 0 \\
6 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 1 & 0 & 0 & 0 & 0 \\
7 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 \\
8 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 0 & 1 & 0 & 0 \\
9 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 0 \\
10 & 0 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 1 \\
11 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & 1 \\
12 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 1 & 0 & 1 \\
13 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 1 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adjacency matrix of cities in Jiangsu Province. Figure 2: The adjacent relationship between cities in Jiangsu Province. as first-order adjacent nodes centered on node 1, and nodes 5 and 6 can be regarded as second-order adjacent nodes centered on node 1. Using the second-order ChebNet graph convolution, the convolution kernel can directly extract information about the surrounding first-order neighboring nodes 2, 3, 4, and second-order neighboring nodes 5, 6 centered on node 1. The detailed implementation process of the second-order ChebNet graph convolution is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Province** & **District Station** & **Station** & **Latitude** & **Liquide (d m)** & **Liquide (d m)** & **Altitude of the** & **Altitude of the** & **Absorimetric Sensor (m)** & **Observation Site (m)** \\ \hline Jiangsu & 58238 & Nanjing & 3156 & 11,854 & 36.4 & 35.2 \\ Jiangsu & 58354 & Wuxi & 3137 & 12,021 & 4.1 & 3.2 \\ Jiangsu & 58027 & Xuzhou & 3417 & 11,709 & 42.0 & 41.2 \\ Jiangsu & 58342 & Changzhou & 3143 & 11,933 & 7.7 & 6.9 \\ Jiangsu & 58349 & Suzhou & 3125 & 12,034 & 8.9 & 8.0 \\ Jiangsu & 58259 & Nantong & 3205 & 12,059 & 4.6 & 4.8 \\ Jiangsu & 58044 & Lianyungang & 3432 & 11,914 & 4.7 & 4.7 \\ Jiangsu & 58141 & Huai’an & 3338 & 11,856 & 13.7 & 12.5 \\ Jiangsu & 58154 & Yancheng & 3326 & 12,012 & 6.8 & 2.5 \\ Jiangsu & 58245 & Yangzhou & 3225 & 11,925 & 14.7 & 9.9 \\ Jiangsu & 58341 & Zhenjiang & 3159 & 11,936 & 10.2 & 6.6 \\ Jiangsu & 58246 & Taizhou & 3233 & 12,000 & 3.4 & 2.6 \\ Jiangsu & 58131 & Suqian & 3358 & 11,813 & 26.0 & 25.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Observation data of basic meteorological elements at ground meteorological stations in Jiangsu Province. Figure 3: First-order GCN implementation process. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Number** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
1 & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0  
Generally, high-order convolution nodes can be achieved by convoluting and stacking multiple low-order nodes. For example, a second-order convolution can be accumulated by two first-order convolutions. When the order is 0, each node can only extract its meteorological features but cannot extract the meteorological features of surrounding nodes. However, the higher the order is not better. When the order is too large, the complexity of the network and the computation time will increase obviously. Taking the graph network of Jiangsu Province as an example, with node 1 as the starting point, the meteorological information of each node in the province can be extracted by 4 times the convolution and superposition of first-order nodes. In this research, it is not necessary to extract the features of all nodes in the research area. According to the experimental verification, the first-order GCN graph convolution operations are performed twice in this research.