###### Abstract  
Noise in large urban areas, which is mainly generated by road traffic and by the human activities carried out nearby and inside the area under study, is a relevant problem. The continuous exposure to high noise levels, in fact, can lead to several problems, largely documented in the scientific literature. The analysis and forecasting of the noise level in a given area are, then, fundamental for control and prevention, especially when field measurements present peculiar trends and slopes, which can be modeled with a Time Series Analysis approach. In this paper, a hybrid model is presented for the analysis and the forecasting of noise time series in urban areas: this technique is based on the application of a deterministic decomposition model followed in cascade by a predictor of the forecasting errors based on an artificial neural network. Two variants of the hybrid model have been implemented and presented. The time series used to calibrate and validate the model is composed of sound pressure level measurements detected on a busy road near the commercial port of an Italian city. The proposed hybrid model has been calibrated on a part of the entire time series and validated on the remaining part. Residuals and error analysis, together with a detailed statistical description of the simulated noise levels and error metrics describe in detail the method's performances and its limitations. acoustical noise; time series analysis; urban pollution; forecasting modeling; neural networks +
Footnote †: journal: Medical  
obtain noise data, it is not always possible to achieve it. When implemented, moreover, such data collections are designed to simply comply with Italian regulations, which only require an equivalent level for diurnal and one for nocturnal hours. For these reasons, many local governments adopt predictive models to forecast noise levels and monitor the acoustic emission by urban sources. In the framework of such models, exposure sound level is usually quantified by some indicators like Noise Equivalent level (Leq) which is formally defined as the sound level in decibels, having the same total sound energy as the fluctuating level measured, and it may be expressed at variable hour intervals. These models, called Road Traffic Noise Models (RTNMs), all have a similar functioning principle: they require as input a set of parameters such as the number of flowing vehicles, speed, vehicle typology (light or heavy), climate variables (humidity, pressure and temperature), the geometry of the area, the presence and position of obstacles, etc., and they give back, as output, a predicted Leq. Among the others, the most important ones to be cited are the Reference Energy Mean Emission Level (REMEL) [14], the SonRoad model [15], the Common Noise Assessment Methods in Europe (CNOSOS-EU) (and its amendments) [16; 17] and the Nouvelle Methode de Prevision du Bruit des Routes (NMPB) [18]. RTNMs work by relying on precise physical rules that need to be implemented in specific computational frameworks. All the procedures of any single RTNMs, in fact, are not fitted into specific easy-to-use software, but their implementation is up to the capability and expertise of the researchers who have to translate them into some programming language. RTNMs, moreover, are forced to work under specific physical assumptions that do not always precisely reflect the real traffic conditions. Many of them, in fact, are forced into the assumption of free flow (a car flowing at a constant speed without overlapping) which is not commonly found in an urban environment. Some RTNMs, moreover, do not take into account the acceleration or deceleration of the vehicles themselves. Such limitations can be overcome by recent approaches, i.e., recurring to the potentiality of Machine Learning (ML) and Artificial Intelligence (AI).