Air quality modelling using chemometric techniques

Azid, Azman; Rani, N. A. A.; Samsudin, Mohd Saiful; Khalit, Saiful Iskandar; Gasim, Muhammad Barzani; Kamarudin, Mohd Khairul Amri; Yunus, Kamaruzzaman; Saudi, Ahmad Shakir Mohd; Yusof, Ku Mohd Kalkausar Ku

doi:10.4314/jfas.v9i2s.30

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…ANN and MLR are the common technique practices and widely applied as the prediction or forecasting tools in multidiscipline, including atmospheric studies (Azid et al 2017). The main difference between ANN and MLR is ANN has the capability to solve the complexity of non-linearity of environmental dataset.…”

Section: Research Articlementioning

confidence: 99%

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Yusof

Azid

Sani³

et al. 2019

Mal. J. Fund. Appl. Sci.

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The comprehensives of particulate matter studies are needed in predicting future haze occurrences in Malaysia. This paper presents the application of Artificial Neural Networks (ANN) and Multiple Linear Regressions (MLR) coupled with sensitivity analysis (SA) in order to recognize the pollutant relationship status over particulate matter (PM10) in eastern region. Eight monitoring studies were used, involving 14 input parameters as independent variables including meteorological factors. In order to investigate the efficiency of ANN and MLR performance, two different weather circumstances were selected; haze and non-haze. The performance evaluation was characterized into two steps. Firstly, two models were developed based on ANN and MLR which denoted as full model, with all parameters (14 variables) were used as the input. SA was used as additional feature to rank the most contributed parameter to PM10 variations in both situations. Next, the model development was evaluated based on selected model, where only significant variables were selected as input. Three mathematical indices were introduced (R2, RMSE and SSE) to compare on both techniques. From the findings, ANN performed better in full and selected model, with both models were completely showed a significant result during hazy and non-hazy. On top of that, UVb and carbon monoxide were both variables that mutually predicted by ANN and MLR during hazy and non-hazy days, respectively. The precise predictions were required in helping any related agency to emphasize on pollutant that essentially contributed to PM10 variations, especially during haze period.

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Section: Research Articlementioning

confidence: 99%

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Yusof

Azid

Sani³

et al. 2019

Mal. J. Fund. Appl. Sci.

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show abstract

“…The parameters were chosen based on our previous study [11,17], as they could give great influence on the performance and physicochemical properties of the prepared catalyst. The model was trained by using the Levenberg Marquardt Learning Algorithm [14,18]. The respected R 2 and RMSE were computed using Eq.…”

Section: Discussionmentioning

confidence: 99%

“…The range calculation for R 2 is in between 0.0−1.0. The lowermost value of R 2 is considered weak, whilst the highest value (near to 1.0) indicates suitability to be chosen as the best predictor [18]. Next, the efficiency model (EM) by Nash and Sutcliffe [19] was used to test the fit between measured and modelled data using the Eq.…”

Section: Discussionmentioning

confidence: 99%

Optimization and physicochemical studies of alumina supported samarium oxide based catalysts using artificial neural network in methanation reaction

Rosid

Azid

Ahmad

et al. 2022

Environmental Engineering Research

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Developed countries are increasing their demand for natural gas as it is an industrial requirement for fuel transportation. Most of modern society relies heavily on vehicles. However, the presence of CO2 gas has led to the categorization of sour natural gas which reduces the quality and price of natural gas. Therefore, the catalytic methanation technique was applied to convert carbon dioxide (CO2) to methane (CH4) gas and reduce the emissions of CO2 within the environment. In this study, samarium oxide supported on alumina doped with ruthenium and manganese was synthesized via wet impregnation. X-ray diffraction (XRD) analysis revealed samarium oxide, Sm2O3 and manganese oxide, MnO2 as an active species. The reduction temperature for active species was at a low reaction temperature, 268.2 o C with medium basicity site as in Temperature Programme Reduction (TPR) and Temperature Programme Desorption (TPD) analyses. Field Emission Scanning Electron Microscopy (FESEM) analysis showed an agglomeration of particle size. The characterised potential catalyst of Ru/Mn/Sm (5:35:60)/Al2O3 (RMS 5:35:60) calcined at 1,000 o C revealed 100% conversion of CO2 with 68.87% CH4 formation at the reaction temperature of 400 o C. These results were verified by artificial neural network (ANN) with validation R 2 of 0.99 indicating all modelling data are acceptable.

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Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

Samsudin

Azid

Khalit

et al. 2019

Marine Pollution Bulletin

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Air quality modelling using chemometric techniques

Cited by 3 publications

References 22 publications

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models over particulate matter (PM10) variability during haze and non-haze episodes: A decade case study

Optimization and physicochemical studies of alumina supported samarium oxide based catalysts using artificial neural network in methanation reaction

Comparison of prediction model using spatial discriminant analysis for marine water quality index in mangrove estuarine zones

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