Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach

Abstract

Noise pollution in densely populated urban areas is a major issue that affects both quality of life and public health. This study explores and evaluates the application of deep learning techniques to predict urban noise levels, using the city of Madrid, Spain, as a case study. Several complementary approaches are compared: Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Graph Convolutional Networks (GCNs). Each technique contributes specific strengths to the modeling of spatiotemporal series: CNNs are effective at capturing local spatial patterns, while LSTM networks excel at modeling long-term temporal dependencies. In turn, GCNs integrate spatial structure and temporal dynamics through graph representations, achieving superior performance compared to traditional approaches or models based solely on CNN or LSTM architectures. This study provides empirical evidence of the potential of GCNs to effectively address the spatiotemporal complexity of urban noise and highlights new possibilities for their application in urban planning and environmental management. Our hybrid model, CNN1D+LSTM+TransformerConv, achieves a root mean squared error (RMSE) of 0.0169, reducing the error by 5.1% compared to the second-best model (Transformer, RMSE = 0.0178), and reaches a correlation coefficient of 0.9601. The results demonstrate that explicitly integrating the spatial component through graphs, alongside temporal sequence modeling, leads to improved prediction accuracy over alternative methods.