Usage of output-dependent data scaling in modeling and prediction of air pollution daily concentration values (PM10) in the city of Konya

Polat, Kemal; Durduran, Süleyman Savaş

doi:10.1007/s00521-011-0661-z

Cited by 21 publications

(13 citation statements)

References 11 publications

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“…The input data vector is (x, x 2 ,…, x n ) T , and output is a y data. The input and output of the hidden layer neurons are shown in equations (2) and 3:…”

Section: Back Propagation Neural Network Algorithmmentioning

confidence: 99%

A New PM2.5 Air Pollution Forecasting Model Based on Data Mining and BP Neural Network Model

Li¹,

Xu²

2018

Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018

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A new PM2.5 air pollution forecasting model based on data mining and BP neural network model was established in this paper. This model combined data mining and BP neural network algorithm with data of mass concentration of PM2.5 and meteorological data obtained from the Ministry of Original data Environmental Protection in China and Anhui Meteorological Data Service Center. The test results showed that the new PM2.5 air pollution forecasting model had higher prediction accuracy than before.

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“…The input data vector is (x, x 2 ,…, x n ) T , and output is a y data. The input and output of the hidden layer neurons are shown in equations (2) and 3:…”

Section: Back Propagation Neural Network Algorithmmentioning

confidence: 99%

A New PM2.5 Air Pollution Forecasting Model Based on Data Mining and BP Neural Network Model

Li¹,

Xu²

2018

Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018

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“…In [7] it is formulated a fuzzy based forecasting model used in Polish Environmental Agency, a model that forecast specific air pollutants based on decades of measurements. Examples of ANFIS based systems for the prediction of air pollutants concentrations are presented in [22], [23], [32] and [33]]. In [23] it is proposed a fuzzy inference system to forecast PM 2.5 concentrations at specific hours using as additional input the medium temperature.…”

Section: An Overview On Pm 25 Computational Intelligence Based Forecmentioning

confidence: 99%

“…The provided FIS model accuracy has the best values for coefficient of determination. For the city of Konya, Turkey the literature present the solution of ANFIS forecasting model for PM 10 trained with large datasets [32]. In [22] there are presented three case studies from three different cities from Romania.…”

Section: An Overview On Pm 25 Computational Intelligence Based Forecmentioning

confidence: 99%

Computational Intelligence-based PM2.5 Air Pollution Forecasting

Oprea¹,

Mihalache²,

Popescu³

2017

INT J COMPUT COMMUN

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Computational intelligence based forecasting approaches proved to be more efficient in real time air pollution forecasting systems than the deterministic ones that are currently applied. Our research main goal is to identify the computational intelligence model that is more proper to real time PM 2.5 air pollutant forecasting in urban areas. Starting from the study presented in [27] a , in this paper we first perform a comparative study between the most accurate computational intelligence models that were used for particulate matter (fraction PM 2.5 ) air pollution forecasting: artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). Based on the obtained experimental results, we make a comprehensive analysis of best ANN architecture identification. The experiments were realized on datasets from the AirBase databases with PM 2.5 concentration hourly measurements. The statistical parameters that were computed are mean absolute error, root mean square error, index of agreement and correlation coefficient.

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“…Following rapid social and economic development over the past several decades, particulate matter and its elemental composition in urban and industrial regions has also become significant in Turkey. Several studies related to monitoring of the PM 10 have been performed at regional and national scales in Turkey (Karaca et al, 2005;Polat and Durduran, 2012;Kara et al, 2013), and the chemical composition and sources of atmospheric PM have also been studied in many parts of the country (Karakas et al, 2004;Yatin et al, 2000;Bayraktar et al, 2011;Durukan et al, 2013). The Anatolia located in the Eastern Mediterranean receives air masses from different directions (Ozturk et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey

Kara

Hopke

Dumanoğlu

et al. 2015

Aerosol Air Qual. Res.

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Source apportionment has most often been applied to a time series of data collected at a single site. However, in a complex airshed where there are multiple sources, it may be helpful to collect samples from multiple sites to ensure that some of them have low contributions from specific sources such that edges can be properly defined. In this study, samples were collected at multiple sites in the Aliaga region (38°40′-38°54′N and 26°50′-27°03′E) located in western Turkey on the coast of the Aegean Sea. This area contains a number of significant air pollution sources including five scrap iron-steel processing plants with electric arc furnaces (EAFs), several steel rolling mills, a petroleum refinery, a petrochemical complex, a natural gas-fired power plant, a fertilizer plant, ship breaking yards, coal storage and packaging, scrap storage and classification sites, large slag and scrap piles, heavy road traffic, very intense transportation activities including ferrous scrap trucks and busy ports used for product and raw material transportation. A total of 456 samples of PM 10 at six sampling sites and 88 samples of PM 2.5 at one site were collected for four seasons and the elemental composition was determined for 43 elements. The newest version of EPA PMF (V5.0) that has the capability of handling multiple site data was used for source apportionment. Eight factors were identified as iron-steel production from scrap (23.4%), resuspended and road dust (23.3%), crustal (20.5%), marine aerosol (14.4%), biomass and wood combustion (7.2%), salvage activities (4.7%), coal combustion (3.7%) and residual oil combustion (2.8%). The pattern of source contributions and conditional probability function analysis were consistent with the locations of the known sources. Thus, the multiple site data allowed for a comprehensive identification of the primary sources of PM in this region.

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Usage of output-dependent data scaling in modeling and prediction of air pollution daily concentration values (PM10) in the city of Konya

Cited by 21 publications

References 11 publications

A New PM2.5 Air Pollution Forecasting Model Based on Data Mining and BP Neural Network Model

A New PM2.5 Air Pollution Forecasting Model Based on Data Mining and BP Neural Network Model

Computational Intelligence-based PM2.5 Air Pollution Forecasting

Characterization of PM Using Multiple Site Data in a Heavily Industrialized Region of Turkey

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