## 2 Related Work  
Urban noise pollution prediction has been addressed using a wide range of methods, from classical statistical techniques to deep-learning-based approaches. Traditional time series models, such as ARIMA and SARIMAX, have been widely used to model acoustic variables by identifying trends and seasonal patterns in historical data. These approaches have proven effective in scenarios where time series are linear and stationary. However, their performance is limited when dealing with non-linear dynamics and the large data volumes characteristic of modern urban environments [5].  
With the rise of deep learning, new methodologies have emerged to overcome the limitations of classical models. Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, have demonstrated strong capabilities in capturing long-term temporal dependencies, significantly improving the accuracy of acoustic predictions [6,7]. Similarly, Convolutional Neural Networks (CNNs) have shown effectiveness in extracting spatial and temporal patterns in complex urban soundscapes [8].  
Recently, hybrid architectures that combine LSTMs with Internet of Things (IoT) technologies have been proposed, enabling short-interval predictions and more dynamic urban noise management. These approaches have outperformed traditional methods such as Random Walk (RW), Stacked Autoencoders (SAEs), and Support Vector Machines (SVMs) in dynamic urban scenarios [7,9].  
Other recent studies have explored the integration of Artificial Neural Networks with Geographic Information Systems (GISs) to explicitly incorporate spatial factors into noise prediction, thereby enhancing responsiveness to local phenomena [10]. Complementary efforts have combined fuzzy logic with autoregressive techniques to manage noise in specific environments, such as construction sites [11].  
An emerging trend in the prediction of dynamic urban phenomena is the use of Graph Neural Networks (GNNs), particularly Graph Convolutional Networks (GCNs). These models enable the representation of spatial relationships through adaptive graphs and attention mechanisms, achieving notable improvements in related tasks such as traffic prediction and air quality forecasting [12, 13, 14, 15, 16].  
Although the direct applications of GCNs to urban noise prediction are still limited, their success in analogous domains suggests substantial potential. For instance, recent approaches using CNNs on traffic flow data have enabled the indirect estimation of noise levels, although these methods face challenges in spatial generalization [17].  
Explicitly modeling urban spatial structure through graphs could help overcome these limitations, allowing for a more robust representation of the spatiotemporal interactions that characterize noise in urban environments.  
In conclusion, despite recent progress, the explicit application of Graph Convolutional Networks (GCNs) to urban noise prediction remains an emerging research direction with considerable promise. Exploring new graph-based spatiotemporal architectures offers the opportunity to improve predictive accuracy, generalization capacity, and dynamic noise management in smart cities. In this study, we propose a hybrid architecture composed of CNN1D, LSTM, and TransformerConv (GCN) layers, which integrates spatiotemporal relationships to forecast future noise levels from real multivariate records, demonstrating significant improvements over previous models.