## 5 Discussion  
When analyzing the results, it is observed that the model CNN1D+LSTM+ TransformerConv outperforms the others in all evaluation metrics, obtaining the lowest RMSE (0.0169) and the highest R\({}^{2}\) (0.8927). In addition, this model achieves the highest correlation (0.9601) and accuracy (0.9158), indicating a superior ability to capture the complex spatio-temporal relationships in noise data. Our model, like GraphSage+LSTM or CNN3D+LSTM, is designed to capture spatio-temporal relationships, but the main difference is that CNN1D+LSTM+TransformerConv captures temporal relationships first and then spatial ones, which could explain its superior performance. In comparison, models like GraphSage+LSTM and GraphSage show relatively good performance, with an RMSE of 0.0294 and correlations close to 0.9. These results indicate that the explicit combination of spatial information via graphs and temporal dependencies via LSTM provides significant predictive value. However, the precision and predictive capacity of these simpler approaches are limited compared to the proposed hybrid model. On the other hand, the CNN3D+LSTM and GAT models show lower performance, with higher errors (RMSE of 0.0340 and 0.0361, respectively) and lower correlation. This may be due to the fact that these architectures fail to effectively capture spatial and temporal relationships simultaneously, possibly due to their structural complexity or limitations in spatial information processing. A noteworthy aspect is the performance of the simple LSTM model, with an RMSE of 0.0305 and a correlation of 0.8939. This suggests that temporal dependencies are key in predicting noise levels and that purely sequential models can capture much of this complexity without explicitly incorporating spatial components. Regarding model performance over multiple time steps, the RMSE fluctuates during the first 10 steps and then shows a steady increase, suggesting a decline in predictive capacity for more distant steps. MAE behaves more erratically, with noticeable peaks around steps 4 and 13, indicating that the model struggles to capture specific patterns. R\({}^{2}\) remains high (0.89) for most steps but shows a slight decreasing trend after step 10, confirming a decline in explanatory power over longer horizons. Correlation initially decreases until step 4, then fluctuates with a peak around step 10, and decreases again in later steps, suggesting difficulty maintaining a linear relationship between real and predicted variables as the forecast horizon is extended. Nevertheless, the metrics and predictions remain accurate, with relatively low error margins. The following figure shows a scatter plot of actual versus predicted values for all time steps. Most points cluster around the red diagonal line, indicating good overall model performance. Although the model captures the general trend, it struggles to precisely predict some extreme values. It is likely that specific and anomalous events, such as protests or other activities that generate noise peaks, challenge the model's predictive ability. Unfortunately, adding variables to help the model anticipate these social factors is difficult due to their variability and unpredictability. When evaluating the model's predictive ability for the L1 variable (representing noise peaks), we found that R\({}^{2}\) drops to 0.7046 and correlation decreases to 0.8892, compared to the 0.8927 and 0.9601 obtained for LAeq. This indicates that although the model can adequately predict average noise levels, its accuracy decreases for extreme