Deep learning and gradient boosting for urban environmental noise monitoring in smart cities

Jérémy Renaud, Ralph Karam, Michel Salomon, Raphaël Couturier

Abstract: Highlights•Urban noise data forecasting is performed using deep learning models and gradient boosting.•Noise data from parking meters deployed in an English city.•Several deep learning models (LSTM, CNN-LSTM, Transformer, TFT) are studied.•Accurate short-term (one hour) and long-term (6 days) forecasts are obtained.•Detection of anomalous data.