Carmine Tepedino

Deterministic decomposition and seasonal ARIMA time series models applied to airport noise forecasting

One of the most hazardous physical polluting agents, considering their effects on human health, is acoustical noise. Airports are a strong source of acoustical noise, due to the airplanes turbines, to the aero-dynamical noise of transits, to the acceleration or the breaking during the take-off and landing phases of aircrafts, to the road traffic around the airport, etc.. The monitoring and the prediction of the acoustical level emitted by airports can be very useful to assess the impact on human health and activities. In the airports noise scenario, thanks to flights scheduling, the predominant sources may have a periodic behaviour. Thus, a Time Series Analysis approach can be adopted, considering that a general trend and a seasonal behaviour can be highlighted and used to build a predictive model. In this paper, two different approaches are adopted, thus two predictive models are constructed and tested. The first model is based on deterministic decomposition and is built composing the trend, that is the ...

Validation of Seasonal ARIMA Models on Road Traffic Noise Measurements

The Time Series Analysis (TSA) technique is largely used in economics and related field, to understand the slope of a given univariate dataset and to predict its future behaviour. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) models are a class of TSA models that, based on the periodicity observed in the series, build a predictive function that can extend the forecast to a given number of future periods. In this paper, these techniques are applied to a dataset of equivalent sound levels, measured in an urban environment. The periodic pattern will evidence a strong influence of human activities (in particular road traffic) on the noise observed. All the three models will exploit the seasonality of the series and will be calibrated on a partial dataset of 800 data. Once the parameters of the models will be evaluated, all the forecasting functions will be tested and validated on a dataset not used before. The performances of all the models will be evaluated in terms of errors values and distributions, such as introducing some error indexes that explain the peculiar features of the models results.

Time series analysis techniques applied to transportation noise

Acoustic noise assessment is a crucial problem in areas in which transportation means, such as motorway, railway, airport, etc., are present. Dwelled areas, in fact, represent a sensible point, that is affected by several externalities, among which, acoustic noise is very important. In this paper, the techniques known as Time Series Analysis (TSA), are used to analyze datasets of noise level produced by transport systems. This approach is based on the analysis of trend and seasonality of the series, and on the implementation of a function of the time that can provide predictions for future time periods. According to the choice and to the input of each model, the forecast horizon can vary from few days further to any time period in the future. Two techniques will be presented: one is based on a Deterministic Decomposition (DD-TSA), able to predict at any future time period; the second is based on a stochastic approach, and adopt the so called SARIMA (Seasonal AutoRegressive Integrated Moving Average) models, to provide prediction on a short time range. Both techniques will be applied to a road traffic noise dataset and to an airport noise levels time series. Results will show that the typology of transportation system does not affect the prediction performances of both the DD-TSA and the SARIMA techniques, even though the time basis of the data is different, being daily for traffic noise and hourly for airport.

A hybrid predictive model for acoustic noise in urban areas based on time series analysis and artificial neural network

The dangerous effect of noise on human health is well known. Both the auditory and non-auditory effects are largely documented in literature, and represent an important hazard in human activities. Particular care is devoted to road traffic noise, since it is growing according to the growth of residential, industrial and commercial areas. For these reasons, it is important to develop effective models able to predict the noise in a certain area. In this paper, a hybrid predictive model is presented. The model is based on the mixing of two different approach: the Time Series Analysis (TSA) and the Artificial Neural Network (ANN). The TSA model is based on the evaluation of trend and seasonality in the data, while the ANN model is based on the capacity of the network to “learn” the behavior of the data. The mixed approach will consist in the evaluation of noise levels by means of TSA and, once the differences (residuals) between TSA estimations and observed data have been calculated, in the training of a ANN ...

Prediction of Airport Acoustical Noise by Deterministic Decomposition and Seasonal ARIMA Techniques

In this paper a time series of hourly equivalent noise levels acquired near the international airport of Nice, France, is analysed. Two different techniques are proposed to model and forecast the time series: deterministic decomposition and seasonal autoregressive moving average. The two models are defined and fitted on the calibration dataset. Subsequently, the developed models are tested comparing their forecasts with 25 noise level data not used in the calibration phase. A detailed error analysis, by means of statistics and metrics, will be presented to test the models performances.