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

Ku Mohd Kalkausar Ku Yusof, Azman Azid, Muhamad Shirwan Abdullah Sani, Mohd Saiful Samsudin, Siti Noor Syuhada Muhammad Amin, Nurul Latiffah Abd Rani, Mohd Asrul Jamalani


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.


Haze, sensitivity analysis, artificial neural network, multiple linear regressions

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Afroz, R., Hassan, M. N. & Ibrahim, N. A. 2003. Review of air pollution and health impacts in Malaysia. Environmental Research, 92(2), 71–77.

Alencar, A., Nepstad, D. & Vera-Diaz, M. d-C. 2006. Forest understory fire in the Brazilian Amazon in ENSO and non-ENSO years: area burned and committed carbon emissions. Earth Interactions, 10(6), 1–17.

Amran, M. A., Azid, A., Juahir, H., Toriman, M. E., Mustafa, A. D., Hasnam, C. N. C., Azaman, F., Kamarudin, M. K. A., Saudi, A. S. M. & Yunus, K. 2015. Spatial analysis of the certain air pollutants using environmetric techniques. Jurnal Teknologi. 75(1), 241 – 249.

Ash, K.D. & Matyas, C.J. 2012. The influences of ENSO and the subtropical Indian Ocean Dipole on tropical cyclone trajectories in the southwestern Indian Ocean. International Journal of Climatology. 32(1), 41–56.

Ashok, K., Guan, Z. & Yamagata, T. 2001. Impact of the Indian Ocean Dipole on the Relationship between the Indian Monsoon Rainfall and ENSO. Geophysical Research Letters. 28(23), 4499–4502.

Azid, A., Juahir, H., Latif, M.T., Zain, S.M. & Osman, M.R. 2013. Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia. Journal of Environmental Protection, 4(12A), 1-10.

Azid, A, Juahir, H, Toriman, M, Kamarudin, M.K.A., Saudi, A.S.M. & Hasnam, C.N.C, 2014. Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: a case study in Malaysia. Water Air Soil Pollution 225(8), 1-14.

Azid, A. Hasnam, C.N.C, Saudi, A.S.M. & Yunus, K. 2015a. Source apportionment of air pollution: A case study in Malaysia. Jurnal Teknologi. 1, 83–88.

Azid, A., Juahir, H., Ezani, E., Toriman, M.E., Endut, A., Rahman, M.N.A., Yunus, K., Kamarudin, M.K.A., Hasnam, C.N.C., Saudi, A.S.M. & Umar, R. 2015b. Identification source of variation on regional impact of air quality pattern using chemometrics. Aerosol and Air Quality Research. 15, 1545–1558.

Azid, A., Juahir, H., Amran, M. A., Suhaili, Z., Osman, M. R., Muhamad, A., Abidin, I. Z., Sulaiman, N. H. & Saudi, A. S. M. 2015c. Spatial air quality modelling using chemometrics techniques: A case study in Peninsular Malaysia. Malaysian Journal of Analytical Sciences. 19(6), 1415 – 1430.

Azid, A., Juahir, H., Toriman, M., Endut, A., Abdul Rahman, M., Amri Kamarudin, M., Latif, M., Mohd Saudi, A., Che Hasnam, C. & Yunus, K. 2016. Selection of the most significant variables of air pollutants using sensitivity analysis. Journal of Testing and Evaluation. 44 (1), 376-384.

Azid, A., Rani, N.A.A., Samsudin, M.S., Khalit, S.I., Gasim, M.B., Kamarudin, M.K.A., Yunus, K., Saudi, A.S.M. & Yusof, K.M.K.K. 2017. Air quality modelling using chemometric techniques. Journal of Fundamental and Applied Sciences 9(2S), 443-466.

Bandyopadhyay, G. & Chattopadhyay, S. 2007. Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. S. Int. J. Environ. Sci. Technol.

Bhuiyan, M.A.H, Siwar, C., Mohd Ismail, S., & Islam, R. 2012. The role of ecotourism for sustainable development in east coast economic region (ECER), Malaysia. International Journal of Sustainable Development. 3(9), 54-60.

Caselli, M., Trizio, L., Gennaro, G. De, & Ielpo, P. 2009. A Simple Feedforward Neural Network for the PM 10 Forecasting : Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model. Water Air Soil Pollution, 201, 365–377.

Challoner, A., Pilla, F. & Gill, L. 2015. Prediction of indoor air exposure from outdoor air quality using artificial neural network model for inner city commercial buildings. International Journal of Environmental Research and Public Health. 12(12), 15233-15253.

Deni, S. M., Suhaila, J., Zin, W. Z. W. & Jemain, A. A. 2009. Trends of wet spells over Peninsular Malaysia during monsoon seasons. Sains Malaysiana. 38(2), 133–142.

Department of Statistics, Malaysia. 2016. Compendium of Environment Statistics 2016.

Department of Environmental, Malaysia. 1997. A Guide to Air Pollutant Index in Malaysia.

Elangasinghe, M. A., Singhal, N., Dirks, K. N. & Salmond, J. A. 2014. Development of an ANN-based air pollution forecasting system with explicit knowldege through sensitivity analysis. Atmospheric Pollution Research. 5, 696–708.

Fearnside, P. M. 1997. Transmigration in Indonesia: Lessons from its environmental and social impacts. Environmental Management. 21(4), 553–570.

Haykin, S. 1999. Neural networks: A comprehensive foundation. Prentice Hall, Ontario.

Isiyaka H A. & Azid A. 2015. Air quality pattern assessment in Malaysia using multivariate techniques. Malaysian Journal of Analytical Sciences. 19(5), 966-978.

Jaafar, S. A., Latif, M. T., Razak, I. S., Shaharudin, M. Z., Khan, M. F., Wahid, N. B. A. & Suratman, S. 2016. Monsoonal variations in atmospheric surfactants at different coastal areas of the Malaysian Peninsula. Marine Pollution Bulletin. 109(1), 480–489.

Jones, C., Peterson, P. & Gautier, C. 1999. A new method for deriving ocean surface specific humidity and air temperature : An artificial neural network approach. American Meteorological Society. 38, 1229–1245.

Juneng, L., Latif, M. T. & Tangang, F. 2011. Factors influencing the variations of PM10 aerosol dust in Klang Valley, Malaysia during the summer. Atmospheric Environment. 45(26), 4370–4378.

Kartawinata, K., Riswan, S., Gintings, A.N. & Puspitojati, T. 2001. An overview of post-extraction secondary forests in Indonesia. Journal Tropical Forest Science. 13(4), 621–638.

Koe, L. C. C., Arellano, A. F. & McGregor, J. L. 2001. Investigating the haze transport from 1997 biomass burning in Southeast Asia: Its impact upon Singapore. Atmospheric Environment. 35(15), 2723–2734.

Latif, M. T., Dominick, D., Ahamad, F., Khan, M. F., Juneng, L., Hamzah, F. M. & Nadzir, M. S. M. 2014. Long term assessment of air quality from a background station on the Malaysian Peninsula. Science of the Total Environment. 482, 336–348.

Le-Dimet, F.-X., Souopgui, I. & Ngodock, H. E. 2017. Sensitivity analysis applied to a variational data assimilation of a simulated pollution transport problem. International Journal for Numerical Methods in Fluids. 83(5), 465–482.

Luo, J. J., Zhang, R., Behera, S. K., Masumoto, Y., Jin, F. F., Lukas, R. & Yamagata, T. 2010. Interaction between El Nino and extreme Indian Ocean dipole. Journal of Climate. 23(3), 726–742.

Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M.T., Aris, A. Z., Zain, S. M., & Dominick, D. 2013. Spatial and temporal air quality pattern recognition using environmetric techniques: A case study in Malaysia. Environmental Science, Processes & Impacts. 15(9), 1717-28.

Nasir, M.F.M., Juahir, H., Roslan, N., Mohd, I., Shafie, N.A., & Ramli, N. 2011. Artificial neural networks combined with sensitivity analysis as a prediction model for water quality index in Juru River, Malaysia. International Journal of Environmental Protection.1(3), 1-8.

Nathan, N. S., Saravanane, R. & Sundararajan, T. 2017. Application of ANN and MLR Models on groundwater quality using CWQI at Lawspet , Puducherry in India. Journal of Geoscience and Environment Protection. 5(3), 99–124.

Nayagam, L. R., Rajesh, J., & Ram Mohan, H. S. (2013). The influence of Indian Ocean sea surface temperature on the variability of monsoon rainfall over India. International Journal of Climatology. 33(6), 1482–1494.

Olden, J. D. & Jackson, D A. 2002. Illuminating the “ Black Box ”: A randomization approach for understanding variable contributions in artificial neural networks networks. Ecological Modelling. 154(1-2), 135-150.

Olden, J. D., Joy, M. K. & Death, R. G. 2004. An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling. 178(3-4), 389–397.

Özdemir, U. & Taner, S. (2014). Impacts of meteorological factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches. Environmental Forensics. 15(4), 329-336.

Ozgoren, M., Bilgili, M. & Sahin, B. 2012. Estimation of global solar radiation using ANN over Turkey. Expert Syst. Appl. 39, 5043–5051.

Payus, C., Abdullah, N. & Sulaiman, N. 2013. Airborne particulate matter and meteorological interactions during the haze period in Malaysia. International Journal of Environmental Science and Development. 4(4), 398–402.

Ramsey, N. R., Klein, P. M. & Moore, B. 2014. The impact of meteorological parameters on urban air quality. Atmospheric Environment. 86, 58–67.

Rani, N.L.A., Azid, A., Khalit, S.I. & Juahir, H. 2018. Prediction model of missing data: A case study of PM10 across Malaysia region. Journal of Fundamental and Applied Sciences, 10(1S), 182-203.

Shaadan, N., Jemain, A. A., Latif, M. T. & Deni, S. M. 2015. Anomaly detection and assessment of PM10 functional data at several locations in the Klang Valley, Malaysia. Atmospheric Pollution Research. 6(2), 365–375.

Shirsath, P.B. & Singh, A.K. 2010. A comparative study of daily pan evaporation estimation using ANN, Regression and Climate Based Models. Water Resource Management 24, 1571-1581.

Suhaila, J., Deni, S. M., Zin, W. Z. W. & Jemain, A. A. 2010. Trends in Peninsular Malaysia rainfall data during the Southwest monsoon and Northeast monsoon seasons: 1975-2004. Sains Malaysiana. 39(4), 533–542.

Sulong, N.A., Latif, M.T., Khan, M.F., Amil, N., Ashfold, M.J., Abdul Wahab, M.I., Chan, K.M. & Sahani, M. 2017. Source apportionment and health risk assessment among specific age groups during haze and non-haze episodes in Kuala Lumpur, Malaysia. Science of the

Total Environment. 601–602, 556–570.

Sunderlin, W. D. & Resosudarmo, I. A. P. 1996. Rates and causes of deforestation in Indonesia: Towards a resolution of the ambiguities. Center for International Forestry Research Occasional. 9(E), 1-23.

Tangang, F. T., Juneng, L., Salimun, E., Vinayachandran, P. N., Seng, Y. K., Reason, C. J. C., Behera, S. K. & Yasunari, T. 2008. On the roles of the northeast cold surge, the Borneo vortex, the Madden-Julian Oscillation, and the Indian Ocean Dipole during the extreme 2006/2007 flood in Southern Peninsular Malaysia. Geophysical Research Letters. 35(14), 1–6.

Tangang, F., Latif, M. T. & Juneng, L. 2010. The roles of climate variability and climate change on smoke haze occurrences in the Southeast Asia Region. London: LSE IDEAS.

Tosun, E., Aydin, K. & Bilgili, M. 2016. Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal. 55(4), 3081-3089.

Yoo, S. H., Yang, S. & Ho, C. H. 2006. Variability of the Indian Ocean sea surface temperature and its impacts on Asian-Australian monsoon climate. Journal of Geophysical Research Atmospheres. 111(3), 1–17.

Yusof, K.M.K.K, Azid, A. & Jamalani, M.A. 2018. Determination of significant variables to particulate matter (PM10) variations in northern region, Malaysia during haze episodes (2006-2015). Journal of Fundamental and Applied Sciences, 10(1S), 300-312.



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