# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
## 5 Conclusions  
It is difficult to forecast future weather changes because of the nonlinear characteristics and spatial-temporal heterogeneity of meteorological time-series data. For accurate and effective temperature prediction, GCN and BiLSTM were integrated to construct the GCN-BiLSTM temperature prediction model in this research. Then, the influence of multiple meteorological elements and time-sliding windows on the forecast results was analyzed and compared among the four models. By analyzing the influence of multiple meteorological elements and time-sliding windows on the forecast results and the forecast performance of each temperature forecast model on meteorological time series from different time and space perspectives, this research draws the following conclusions. (1) Multidimensional meteorological features were used to replace single meteorological features in the GCN-BiLSTM model. The sliding time window setting was added to extend the model input data dimension and enable the model to further learn the correlation of multidimensional meteorological elements and temporal characteristics of historical meteorological data on future temperatures and improve prediction accuracy. (2) Compared to baseline DL models, the prediction effect of the GCN-BiLSTM model was significantly improved, benefiting from the time series and spatial features of the meteorological data. GCN-BiLSTM model had the lowest RMSE and MAE values, which can accurately predict the future temperature with \(\pm\)2 \({}^{\circ}\)C average errors. (3) From the perspective of forecast time, the prediction accuracy of each model decreases as the number of prediction days increases. Among them, the error curve of the  
Figure 12: Spatial distribution of temperature prediction errors in Jiangsu Province. GCN-BiLSTM-based prediction is relatively smooth. From the perspective of space, the prediction effect of the GCN-BiLSTM model is influenced by the meteorological factors of neighboring regions. The prediction results of each model in areas with smaller annual temperature differences are better than those with larger annual temperature differences. In the future, we plan to enhance this research in the following ways. First, this research makes model performance comparisons for temperature prediction in different regions and analyzes the spatial performance differences of the models. Most of China's areas are located in the temperate monsoon climate zone, the subtropical monsoon climate zone, and the temperate continental climate zone, with noticeable seasonal changes. In future research, the performance differences of the model will be considered in different seasons. In addition, controlled experiments can be conducted and analyzed on season or month scales. Second, only adjacency is considered a spatial feature for constructing the experimental graph network in this research, in which the spatial feature relationship is relatively simple. Furthermore, in this research, the station temperature is used to represent the temperature of the city where the station is located, which should be further optimized on a smaller scale. For example, we can generate large amounts of raster data to describe the spatial feature relationship of meteorological time series by further interpolation methods from points to surfaces. Conceptualization, L.M. ; methodology, L.M. ; software, D.Y. ; validation, Y.P. and Y.Z. ; formal analysis, Y.Z. ; investigation, Y.Z. ; resources, L.M. ; data curation, D.Y. and Y.Z. ; writing--original draft preparation, D.Y. and Y.Z. ; writing--review and editing, L.M. and Y.P. ; visualization, D.Y. and Y.Z. ; supervision, Y.P. and L.M. ; project administration, Y.P. and L.M. ; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript. This research was funded by the Opening Foundation of Ministry of Education of Key Lab of Virtual Geographic Environment (Grant No.