# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 4 Results and Discussion
### Discussion  
From the 14-day temperature prediction experiments, it can be seen in Table 6 that for the average RMSE and MAE of the temperature forecast for the next 14 days, the GCN-BiLSTM model reaches 2.671 \({}^{\circ}\)C and 3.024 \({}^{\circ}\)C, respectively. Compared to LSTM, DFN, and ARIMA models, the RMSE value decreased by 23.45%, 38.16%, and 46.48%; the MAE value decreased by 32.61%, 34.15%, and 47.35%, respectively. The RMSE and MAE variations of the four models are shown in Figures 7 and 8. It can be seen that the GCN-BiLSTM model achieves the best prediction performance for different predictions in different experimental regions. Specifically, as the number of forecast days increases, both the RMSE and MAE errors of these models appear to have an increasing trend. The ARIMA model outperforms the DFN and LSTM methods in terms of RMSE for short forecast days. However, due to its limitation of single attribute prediction, ARIMA performs poorly in longer time-step prediction. As shown in Figure 7, the ARIMA error exceeds the DFN, with the highest error from day 5, and the error tends to increase significantly afterward. This indicates that the DFN model and the GCN-BiLSTM model, which integrate multiple influencing factors, have better performance than the ARIMA model, which only considers single factors. After the 7 th day, the RMSE and MAE of the DFN model continue to get higher. This is because the DFN model does not analyze the time-series features of temperature data and performs poorly in long-term temperature prediction. From the 7 th day to the 14 th day, the LSTM model and the GCN-BiLSTM model, which take into account the time series features, show better performance in long-term temperature prediction. Their RMSE and MAE are relatively lower, and the change in errors is smaller. Further, compared with the LSTM model, which only considers temporal features, the average RMSE and MAE of GCN-BiLSTM decrease by 23.45% and 32.61%, respectively, in the long-short term predictions. This demonstrates that the GCN-BiLSTM model, considering spatial-temporal features, has better prediction performance. This is because it captures the influence of meteorological factors of the surrounding area on the target area from the perspective of urban spatial distribution. Meanwhile, the information containing spatial features is also modeled based on temporal features, and a DL model with spatial-temporal feature capture capability is constructed. Overall, the GCN-BiLSTM model performed better in the learning of temporal and spatial features., and has the best performance in long-short term temperature forecasting. From the multi-regional temperature prediction experiments, as shown in Table 8, the average RMSE of the GCN-BiLSTM model is 1.906 \({}^{\circ}\)C, which is 26.35%, 33.79%, and 38.73% lower than that of the LSTM, ARIMA, and DFN models, respectively. Similarly, in terms of absolute error, the average MAE of the GCN-BiLSTM model is 2.583 \({}^{\circ}\)C, which is 15.09%, 26.87%, and 38.18% lower than that of LSTM, DFN, and ARIMA, respectively, as shown in Table 9.  
The RMSE and MAE of different models in different cities are shown in Figures 9 and 10. As can be seen in Figure 9, the GCN-BiLSTM model outperforms ARIMA, LSTM, and DFN in temperature prediction in different regions. As in the above analysis, the ARIMA model outperforms the DFN model in terms of RMSE in all research cities. However, for MAE, the result is the opposite. The LSTM model still performs better than the ARIMA model and the DFN model. Comprehensively, the prediction accuracy of the four models has a similar trend, but the GCN-BiLSTM model is more adaptive in each region. This is because the GCN-BiLSTM model can capture the spatial-temporal features of the temperature data in the current region. Specifically, the RMSE of the GCN-BiLSTM model in Hangzhou, Qingdao, Guangzhou, and Shenzhen are lower than the average of the eight cities, while the RMSE in Harbin, Beijing, Jinan, and Nanjing are higher than the average.