Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Bai, Yun; Li, Yong; Wang, Xiaoxue; Xie, Jingjing; Li, Chuan

doi:10.1016/j.apr.2016.01.004

Cited by 260 publications

(83 citation statements)

References 43 publications

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“…ANN, which has the capabilities of nonlinear mapping, self-adaption, and robustness, has proved its superiority and is widely used in forecasting fields. Recently, various structures of the ANN have been developed for improving the forecasting performances of air pollutant concentrations (Bai et al 2016). Results in this work confirm that the ANN in air pollution forcasting generally gives better results than linear methods.…”

Section: Linear and Nonlinear Methods For Air Pollution Forecastingsupporting

confidence: 64%

“…The public is informed of air quality index (AQI) calculated from air pollutants concentrations forecasted and associated health risks through government announcements (Zhang et al 2012). Therefore, an accurate and reliable model for forecasting air pollutant concentrations is important since it can provide advanced air pollution information at an early stage such that guiding the works of air pollution control and public health protection (Bai et al 2016).…”

Section: Why the Air Pollution Forecasting Is Important?mentioning

confidence: 99%

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Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Rahimi

2017

Ecol Process

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Introduction: Due to the health effects caused by airborne pollutants in urban areas, forecasting of air quality parameters is one of the most important topics of air quality research. During recent years, statistical models based on artificial neural networks (ANNs) have been increasingly applied and evaluated for forecasting of air quality. Methods: The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO 2 and NO x concentrations as a function of meteorological conditions. The optimum structure of ANN was determined by a trial and error method. We used hourly NO x and NO 2 concentrations and metrological parameters, automatic monitoring network during October and November 2012 for two monitoring sites (Abrasan and Farmandari sites) in Tabriz, Iran. Results: Designing of the network architecture is based on the approximation theory of Kolmogorov, and the structure of ANN with 30 neurons had the best performance. ANN trained by scaled-conjugate-gradient (trainscg) training algorithm has implemented to model. It also demonstrates that MLP neural networks offer several advantages over linear MLR models. The results show that the correlation coefficient (R 2 ) values are 0.92 and 0/94 for NO 2 and NO x concentrations, respectively. But in MLR model, R 2 values were 0.41 and 0.44 for NO 2 and NO x concentrations, respectively. Conclusions: This work shows that MLP neural networks can accurately model the relationship between local meteorological data and NO 2 and NO x concentrations in an urban environment compared to linear models.

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Section: Linear and Nonlinear Methods For Air Pollution Forecastingsupporting

confidence: 64%

Section: Why the Air Pollution Forecasting Is Important?mentioning

confidence: 99%

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Rahimi

2017

Ecol Process

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“…In terms of methodology, descriptive statistics, correlation analysis and principal component analysis have been widely used to study the air pollution problem. Azid A, Juahir H and Toriman ME forecasted the air pollution level by using the method of principal component analysis and artificial neural network; Assareh N, Prabamroong T and Manomaiphiboon K made a statistical analysis of the amount of ozone in the eastern region of Thailand from 1997 to 2012 during dry seasons [7,8] [9,10]. With the advent of the high frequency air quality data, some researchers try to use the data mining method to study the associated rules between different air pollutants.…”

Section: Literature Reviewmentioning

confidence: 99%

The Research on Air Pollution Laws in Guanzhong Urban Agglomeration Based on High Frequency AQI Data

Hu¹,

Yang²

2017

dteees

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Abstract. Based on high frequency data of AQI and contaminants, this paper makes researches including general situation of air pollution, fluctuation rules of air quality in one day and associated rules of air pollution between cities by using statistical analysis methods like the hour index of AQI and building the VAR model. The conclusions are as following: firstly, it is obvious that air pollution which often manifests as pollution of particulates has a seasonal effect and a clustering property. Secondly, different seasons have different air quality fluctuation rules in one day. However, with no consideration of the phase position diversity of graphs, intraday fluctuation rules of air quality in one season are similar to those in other seasons. Thirdly, the deterioration of air condition of one city can cause the deterioration of air condition of other cities, and the peak of this influence appears in one day generally, and the influence weakens with the increase of spatial distance.

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“…According to various recent epidemiological investigations, particulate matter (PM) can seriously affect the health of living things even at relatively low levels in the atmosphere. Pulmonary and cardiovascular diseases such as chronic respiratory problems, eye irritation, shortness of breath and cancer are some of the important and serious health problems caused by PM (Feng et al, 2015;Bai et al, 2016;Evagelopoulos et al, 2006;Ghozikali et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

2018

Issue 2

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In this study, potential of neural network to estimate daily mean PM10 concentration levels in Sakarya city, Turkey as a case study was examined to achieve improved prediction ability. The level and distribution of air pollutants in a particular region is associated with changes in meteorological conditions affecting air movements and topographic features. Thus, meteorological variables data for a two-year period for Sakarya city which is located in most industrialized and crowded part of Turkey were selected as input. Neural network models and multiple linear regression models have been statistically evaluated. The results of the study showed that ANN models were accurate enough for prediction of PM10 levels.

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Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Cited by 260 publications

References 43 publications

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

Short-term prediction of NO2 and NO x concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran

The Research on Air Pollution Laws in Guanzhong Urban Agglomeration Based on High Frequency AQI Data

Forecasting PM10 levels using ANN and MLR: A case study for Sakarya City

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