Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

Azid, Azman; Juahir, Hafizan; Toriman, Mohd Ekhwan; Kamarudin, Mohd Khairul Amri; Saudi, Ahmad Shakir Mohd; Hasnam, Che Noraini Che; Aziz, Norazlina Abdul; Azaman, Fazureen; Latif, Mohd Talib; Zainuddin, Syahrir Farihan Mohamed; Osman, Mohamad Romizan; Yamin, Mohammad

doi:10.1007/s11270-014-2063-1

Cited by 160 publications

(96 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Every component in PCA is orthogonal between each other. The variance of a large set of interrelated variables can be transformed into a new (smaller set of uncorrelated (independent) variables when applying this method [16][17]. In this study, the most significant parameter contributing to IAQ can be recognized by this method.…”

Section: Using Pca To Identify the Most Significant Variablesmentioning

confidence: 99%

Assessing Indoor Air Quality Using Chemometric Models

Azid¹,

Amran²,

Samsudin³

et al. 2018

Pol. J. Environ. Stud.

Self Cite

View full text Add to dashboard Cite

The objectives of this study are to identify the significant variables and to verify the best statistical method for determining the effect of indoor air quality (IAQ) at 7 different locations in Universiti Sultan Zainal Abidin, Terengganu, Malaysia. The IAQ data were collected using in-situ measurement. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discrimination analysis (LDA), and agglomerative hierarchical clustering (AHC) were used to classify the significant variables as well as to compare the best method for determining IAQ levels. PCA verifies only 4 out of 9 parameters (PM 10 , PM 2.5 , PM 1.0 , and O 3 ) and is the significant variable in IAQ. The PLS-DA model classifies 89.05% correct of the IAQ variables in each station compared to LDA with only 66.67% correct. AHC identifies three cluster groups, which are highly polluted concentration (HPC), moderately polluted concentration (MPC), and low-polluted concentration (LPC) area. PLS-DA verifies the groups produced by AHC by identifying the variables that affect the quality at each station without being affected by redundancy. In conclusion, PLS-DA is a promising procedure for differentiating the group classes and determining the correct percentage of variables for IAQ.

show abstract

“…All variables are considered adequate and can implement for further analysis. Correlations between variables of air quality and the extracted factors can be assess from applying factor loadings [28]. Besides, according to [11], principal component/factor analysis may be convenient if high value which is close to 1 obtained.…”

Section: N L Abd Rani Et Al J Fundam Appl Sci 2017 9(2s) 335-351mentioning

confidence: 99%

Selected Malaysia air quality pollutants assessment using chemometrics techniques

Rani¹,

Azid²,

Khalit³

et al. 2018

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

Air quality played an important role as polluted air quality could environment as well as property.chemometrics was performed with the objectives to ensure the air quality data analysis is Air quality played an important role as polluted air quality could harm human health, environment as well as property. Thus, a study of air quality pollutants assessment using chemometrics was performed with the objectives to ensure the air quality data analysis is le and interpreted well. Analysis of PCA, FA, KMO and Bartlett's test were done on five main air quality pollutants (O 3 , NO 2 , SO 2 , CO and PM 10 From the data analysis obtained, the concentrations of air quality pollutants all around Malaysia starting from 2008 to 2011 were acceptable and the most dominant major pollutants had been highlighted. KMO obtained in this study is 0.7760, which show that the results are factor well. While, Bartlett's test shows that the variables correlated to each other's.quality data were acceptable for factor analysis.

show abstract

Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: a Case Study in Malaysia

Cited by 160 publications

References 60 publications

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

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

Assessing Indoor Air Quality Using Chemometric Models

Selected Malaysia air quality pollutants assessment using chemometrics techniques

Contact Info

Product

Resources

About