Sensitivity analysis and ensemble artificial intelligence-based model for short-term prediction of NO 2 concentration
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ORIGINAL PAPER
Sensitivity analysis and ensemble artificial intelligence‑based model for short‑term prediction of NO2 concentration V. Nourani1,2 · Z. Abdollahi1 · E. Sharghi1 Received: 28 April 2020 / Revised: 27 September 2020 / Accepted: 31 October 2020 © Islamic Azad University (IAU) 2020
Abstract In this study, in the first step, three scenarios with different input combinations are created to implement a sensitivity analysis for hourly NO2 prediction in Columbus City, Ohio. Three classes of inputs including concentration-related data (NO2 concentration at previous time steps and N O2 concentration in the suburban monitoring station), meteorology (wind speed, wind direction, and temperature), and traffic-related data (traffic count, hour of the day, and day of the week) are applied to create three scenarios. Also, the support vector regression methodology is employed to perform the sensitivity analysis. Dominant variables determined in the sensitivity analysis are applied as inputs to three models called feed-forward neural network, support vector regression, as well as classification and regression tree. In the last step, ensemble techniques including simple linear averaging, weighted linear averaging, and nonlinear support vector regression ensemble are proposed to improve the performance of sole models. The results indicate that, in the urban area, in addition to N O2 variations in the previous time step, other variables such as hourly traffic count in freeway loop, suburban N O2 concentration, and hour of the day can affect the NO2 concentration. Further, the values of determination coefficient for the individual models, namely classification and regression tree and feed-forward neural network, are 67 and 81% that the ensemble technique as a post-processing approach enhances the performance of them up to 19% and 5% in the verification steps, respectively. Keywords Air pollution modelling · Traffic-related pollutant · Support vector regression · Feed-forward neural network · Classification and regression tree · Columbus City
Introduction Air pollution is a serious challenge worldwide, especially in highly populated areas such as metropolises with heavy traffic flows. Due to the development of transportation and urbanization, the number of vehicles has increased tremendously and traffic-related pollution has become one of the major concerns. The role of traffic in producing pollutants Editorial responsibility: Parveen Fatemeh Rupani. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s13762-020-03002-6) contains supplementary material, which is available to authorized users. * V. Nourani [email protected] 1
Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran
Faculty of Civil and Environmental Engineering, Near East University, via Mersin 10, 99138 Nicosia, N Cyprus, Turkey
2
such as nitrogen oxides ( NOx), carbon monoxide (CO), and aromatic hydrocarbons in urban environments i
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