In brief
Urban noise has both a temporal rhythm and a spatial structure. The study compares deep-learning approaches that represent those two dimensions explicitly across Madrid.
Contribution
A hybrid CNN1D, LSTM and graph-transformer model evaluated against temporal, convolutional and graph-based alternatives.
Key finding
Explicit graph structure produced the best prediction accuracy, reaching an RMSE of 0.0169 and a correlation coefficient of 0.9601.
Models and data
Distinctive research elements
Models and methods
- CNN1D+LSTM+TransformerConv
- Transformer
Data and evaluation
- Madrid urban-noise observations
Author-written overview
Research overview
Urban noise varies across both time and the structure of a city. Using Madrid as a case study, this research compares convolutional, recurrent and graph-based deep-learning approaches for noise prediction. The models test complementary ways of representing local patterns, longer temporal dependencies and relationships between observation locations. The hybrid CNN1D+LSTM+TransformerConv architecture produces the strongest result, with an RMSE of 0.0169 and a correlation coefficient of 0.9601. Its error is 5.1% lower than that of the second-best evaluated model. The comparison provides evidence that an explicit graph representation adds useful spatial information to temporal sequence modelling for urban-noise forecasting.
Cite this work
BibTeX
@article{semper2025noise,
title = {Noise Pollution Prediction in a Densely Populated City Using a Spatio-Temporal Deep Learning Approach},
author = {Marc Semper and Manuel Curado and Jose Luis Oliver and Jose F. Vicent},
journal = {Applied Sciences},
year = {2025},
volume = {15},
number = {10},
eid = {5576},
doi = {10.3390/app15105576}
}Always check the publisher record for final volume, issue and page information before citing.