Applications of AutoRegressive Integrated Moving Average (ARIMA) approach in time-series prediction of traffic noise pollution

Abstract

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 L Day (06-22 h) and L Night (22-06 h) in A-and C-weightings in conjunction with single-noise metrics, daynight 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. © 2015 Institute of Noise Control Engineering.

Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions

“…There has been no such previous study reported on the development of an ANN model for Delhi city, while for the other cities of [44][45][46][47][48][49]. Thus, it can provide a suitable substitute to cumbersome long-term noise monitoring and forecasting [50,51]. However, for such predictions, an exclusive and precise database of hourly vehicular density for each type of vehicles moving on roads, average speed of light and heavy vehicles etc., is a pre-requisite.…”

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

“…IoT must overcome some challenges to extract new insights from data [49]. In previous studies [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], environmental noise prediction mainly focused on the spatial propagation of noise, and there were few studies focused on the variation of noise in short-term. The noise data set used in this study is in seconds, which is more random.…”

“…In recent years, with the widespread utilization of sound level meters and the development of various sensor network technologies, environmental noise data has an exploding expansion. Although there have been previous studies on noise measurement, prediction, and control [19,22,23,25,[33][34][35][36], most of the research data are relatively diminutive. This gave the inspiration to have a second thought about the environmental noise prediction problem, that is, whether there are more optimized noise prediction models and methods when handling an abundant amount of noise data.…”

Modified dam deformation monitoring model considering periodic component contained in residual sequence

“…In addition, considering that the residual sequence contains certain noise and periodic components, a modified modeling method by extracting periodic component from the residual sequence of conventional statistical model is proposed in the study. Inspired by the application of singular spectrum analysis (SSA) in data processing [27][28][29][30] and the application of autoregressive integrated moving average (ARIMA) model in time series analysis, [31][32][33][34] the residual sequence obtained by conventional statistical model is processed and forecasted by SSA and ARIMA. Firstly, the conventional statistical model is established with stepwise regression model, and the residual sequence is reconstructed using the trend and periodic components extracted by SSA.…”