Garg, N. and Soni, K. and Saxena, T. K. and Maji, S. (2015) Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution. Noise Control Engineering Journal, 63 (2). pp. 182-194. ISSN 0736-2501

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The paper analyzes the long-term noise monitoring data using the AutoRegressive Integrated Moving Average (ARIMA) modeling technique. Box-Jenkins ARIMA approach has been adapted to simulate the daily mean LDay (06-22 h) and L-Night (22-06 h) in A-and C-weightings in conjunction with single-noise metrics, day-night average sound level (DNL) for a period of 6 months. The autocorrelation function (ACF) and partial autocorrelation function (PACF) have been obtained to find suitable orders of autoregressive (p) and moving average (q) parameters for ARIMA (p, d, q) models so developed for all the single-noise metrics. The ARIMA models, namely, ARIMA(0,0,14), ARIMA(0,1,1), ARIMA(7,0,0), ARIMA(1,0,0) and ARIMA(0,1,14), have been developed as the most suitable for simulating and forecasting the daily mean L-Day dBA, L-Night dBA, L-Day dBC, L-Night dBC, and day-night average sound level (DNL) respectively. The performance of the model so developed is ascertained using the statistical tests. The work reveals that the ARIMA approach is reliable for time-series modeling of traffic noise levels.

Item Type: Article
Subjects: Acoustics
Depositing User: Dr. Rajpal Walke
Date Deposited: 16 Sep 2016 06:55
Last Modified: 16 Sep 2016 06:55

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