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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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2024-08-27 14:24:58
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Restricted Document
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12879
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UniSZA
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[1] Azid, A., Juahir, H., Latif, M. T., Zain, S. M., and Osman, M. R., “Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia,” J. Environ. Prot., Vol. 4, No. 12, 2013, pp. 1–10. [2] Dominick, D., Juahir, H., Latif, M. T., Zain, S. M., and Aris, A. Z., “Spatial Assessment of Air Quality Patterns in Malaysia Using Multivariate Analysis,” Atmos. Environ., Vol. 60, 2012, pp. 172–181. [3] Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M. T., Aris, A. Z., Zain, S. M., and Dominick, D., “Spatial and Temporal Air Quality Pattern Recognition Using Environmetric Techniques: A Case Study in Malaysia,” Environ. Sci. Process Impacts, Vol. 15, No. 9, 2013, pp. 1717–1728. [4] Abdullah, A. M., Samah, M. A. A., and Tham, Y. J., “An Overview of the Air Pollution Trend in Klang Valley, Malaysia,” Open Environ. Sci., Vol. 6, 2012, pp. 13–19. [5] Afroz, R., Hassan, M. N., and Ibrahim, N. A., 2003, “Review of Air Pollution and Health Impacts in Malaysia,” Environ. Res., Vol. 92, No. 2, pp. 71–77. [6] Azmi, S. Z., Latif, M. T., Ismail, A. S., Juneng, L., and Jemain, A. A., 2010, “Trend and Status of Air Quality at Three Different Monitoring Stations in the Klang Valley, Malaysia,” Air Qual. Atmos. Health, Vol. 3, No. 1, pp. 53–64. [7] Azid, A., Juahir, H., Aris, A. Z., Toriman, M. E., Latif, M. T., Zain, S. M., Yusof, K. M. K. K., and Saudi, A. S. M., “From Sources to Solution,” Proceedings of the International Conference on Environmental Forensics 2013, A. Z. Aris, T. H. T. Ismail, R. Harun, A. M. Abdullah, and M. Y. Ishak, Eds., Springer, New York, 2014, 307 pp. [8] Wahid, N. B. A., Latif, M. T., and Suratman, S., “Composition and Source Apportionment of Surfactants in Atmospheric Aerosols of Urban and Sub-Urban Areas in Malaysia,” Chemosphere, Vol. 91, No. 11, 2013, pp. 1508–1516. [9] Rai, R., Rajput, M., Agrawal, M., and Agrawal, S. B., “Gaseous Air Pollutants: A Review on Current and Future Trends of Emissions and Impact on Agriculture,” J. Sci. Res., Vol. 55, 2011, pp. 77–102. [10] Balasubramanian, R., Qian, W. B., Decesari, S., Facchini, M. C., and Fuzzi, S., “Comprehensive Characterization of PM2.5 Aerosols in Singapore,” J. Geophys. Res., Vol. 108, No. D16, 2003, p. 4523. [11] Jamal, H. H., Pillay, M. S., Zailina, H., Shamsul, B. S., Sinha, K., Zaman Huri, Z., Khew, S. L., Mazrura, S., Ambu, S., Rahimah, A., and Ruzita, M. S., 2004, “A Study of Health Impact & Risk Assessment of Urban Air Pollution in Klang Valley,” UKM Pakarunding Sdn. Bhd., Kuala Lumpur, Malaysia. [12] Rahman, N. H. A., Lee, M. H., Latif, M. T., and Suhartono, S., “Forecasting of Air Pollution Index With Artificial Neural Network,” J. Teknol., Vol. 63, No. 2, 2013, pp. 59–64. [13] Bishoi, B., Prakash, A., and Jain, V. K., “A Comparative Study of Air Quality Index Based on Factor Analysis and US-EPA Methods for an Urban Environment,” Aero. Air Qual. Res., Vol. 9, No. 1, 2009, pp. 1–17. [14] Department of the Environment, “Air Pollutant Index (API),” 2013, http://www.doe.gov.my/webportal/en/info-umum/englishair-pollutant-index-api/. Accessed on 1 August 2014. [15] Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C., Aziz, N. A. A., Azaman, F., Latif, M. T., Zainuddin, S. F. M., Osman, M. R., and Yamin, M., “Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia,” Water Air Soil Pollut., Vol. 225, No. 8, 2014, p. 2063. [16] Alkasassbeh, M., Sheta, A. F., Faris, H., and Turabieh, H., “Prediction of PM10 and TSP Air Pollution Parameters Using Artificial Neural Network Autoregressive, External Input Models: A Case Study in Salt, Jordan,” Middle-East J. Sci. Res., Vol. 14, No. 7, 2013, pp. 999–1009. [17] Suh, S. C., Practical Application of Data Mining, Jones & Bartlett Learning, Sudbury, MA, 2012. [18] Xie, H., Ma, F., and Bai, Q., “Prediction of Indoor Air Quality Using Artificial Neural Networks,” 2009 Fifth International Conference on Natural Computation, Tianjin, China, August 14–16, 2009, IEEE Computer Society, Washington, DC, pp. 414–418. [19] Karatzas, K. and Kaltsatos, S., “Air Pollution Modelling With the Aid of Computational Intelligence Methods in Thessaloniki, Greece,” Simulation Modelling Practice and Theory, Vol. 15, No. 10, 2007, pp. 1310–1319. [20] Palani, S., Tkalich, P., Balasubramanian, R., and Palanichamy, J., “ANN Application Prediction of Atmospheric Nitrogen Deposition to Aquatic Ecosystems,” Marine Pollut. Bull., Vol. 62, No. 6, 2011, pp. 1198–1206. [21] Zhang, G., Eddy Patuwo, B., and Hu, M. Y., “Forecasting With Artificial Neural Networks: The State of the Art,” Int. J. Forecast., Vol. 14, No. 1, 1998, pp. 35–62. [22] Kurt, A., Gulbagai, B., Karaca, F., and Alagha, O., “An Online Air Pollution Forecasting System Using Neural Networks,” Environ. Int., Vol. 34, No. 5, 2008, pp. 592–598. [23] Afzali, M., Afzali, A., and Zahedi, G., “The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction,” Int. J. Environ. Sci. Dev., Vol. 3, No. 1, 2012, pp. 33–38. [24] Brey, T., Teichmann, A. J., and Borlich, O., “Artificial Neural Network Versus Multiple Linear Regression: Predicting P/B Ratio From Empirical Data,” Marine Ecol. Prog. Ser., Vol. 140, 1996, pp. 251–256. [25] Pao, H. T., “A Comparison of Neural Network and Multiple Linear Regression Analysis in Modelling Capital Structure,” Exp. Syst. Appl., Vol. 35, 2008, pp. 720–727. [26] Zaefizadeh, M., Khayatnezhad, M., and Gholamin, R., “Comparison of Multiple Linear Regression and Artificial Neural Network in Predicting the Yield Using Its Components in the Hulless Barley,” Am.-Eurasian J. Agric. Environ. Sci., Vol. 10, No. 1, 2011, pp. 60–64. [27] Manache, G. and Melching, C. S., “Identification of Reliable Regression- and Correlation-Based Sensitivity Measures for Importance Ranking of Water Quality Model Parameters,” Environ. Mod. Soft., Vol. 23, No. 5, 2008, pp. 549–562. [28] Nasir, M. F. M., Juahir, H., Roslan, N., Mohd, I., Shafie, N. A., and Ramli, N., “Artificial Neural Networks Combined With Sensitivity Analysis as a Prediction Model for Water Quality Index in Juru River, Malaysia,” Int. J. Environ. Prot. Vol. 1, No. 3, 2011, pp. 1–8. [29] Zali, M. A., Retnam, A., Juahir, H., Zain, S. M., Kasim, M. F., Abdullah, B., and Saadudin, S. B., “Sensitivity Analysis for Water Quality Index (WQI) Prediction for Kinta River, Malaysia,” World Appl. Sci. J., Vol. 14, 2011, pp. 60–65. [30] Department of Environment (DOE), “Main Sources of Air Pollution in Malaysia,” http://apims.doe.gov.my/apims/ General%20Info%20of%20Air%20Pollutant%20Index.pdf. Accessed on 1 August 2014. [31] Department of Environment (DOE), A Guide to Air Pollutant Index in Malaysia (API), 3rd ed., DOE, Kuala Lumpur, Malaysia, 1997, 20 pp. [32] Alam Sekitar Malaysia Sdn. Bhd. (ASMA), “Standard Operating Procedure for Continuous Air Quality Monitoring,” Shah Alam, Selangor, Malaysia, 2007. [33] Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J., and Kolehmainen, M., “Methods for Imputation of Missing Values in Air Quality Data Set,” Atmos. Environ., Vol. 38, No. 18, 2004, pp. 2895–2907. [34] Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., and Ramli, M. F., “Artificial Neural Network Modelling of the Quality Index for Kinta River (Malaysia) Using Water Quality Variables as Predictors,” Marine Pollut. Bull., Vol. 64, 2012, pp. 2409–2420. [35] Daliakopoulos, I. N., Coulibaly, P., and Tsanis, I. K., “Groundwater Level Forecasting Using Artificial Neural Networks,” J. Hydrol., Vol. 309, Nos. 1–4, 2005, pp. 229–240. [36] Kisi, O., “Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models,” Turkish J. Eng. Environ. Sci., Vol. 29, 2005, pp. 9–20. [37] Lee, J. H. W., Huang, Y., Dickman, M., and Jayawardena, A. W., “Neural Network Modeling of Coastal Algal Blooms,” Ecol. Model., Vol. 159, Nos. 2–3, 2003, pp. 179–201. [38] Latif, M. T., Azmi, S. Z., Noor, A. D. M., Ismail, A. S., Johny, Z., Idrus, S., Mohamad, A. F., and Mokhtar, M., “The Impact of Urban Growth on Regional Air Quality Surrounding the Langat River Basin, Malaysia,” Environmentalist, Vol. 31, No. 3, 2011, pp. 315–324. [39] Mustafa, M., Syed Abdul Kader, S. Z., and Sufian, A., “Coping With Climate Change Through Air Pollution Control: Some Legal Initiatives From Malaysia,” 2012 International Conference on Environment, Energy and Biotechnology, Kuala Lumpur, Malaysia, May 5–6, 2012, Vol. 33, pp. 101–105. [40] Atkinson, R., “Atmospheric Chemistry of VOCs and NOx,” Atmos. Environ., Vol. 34, 2000, pp. 2063–2101. [41] Juneng, L., Latif, M. T., Tangang, F. T., and Mansor, H., “Spatio-Temporal Characteristics of PM10 Concentration Across Malaysia,” Atmos. Environ., Vol. 43, No. 30, 2009, pp. 4584–4594. [42] Juneng, L., Latif, M. T., and Tangang, F., “Factors Influencing the Variations of PM10 Aerosol Dust in Klang Valley, Malaysia During the Summer,” Atmos. Environ., Vol. 45, No. 26, 2011, pp. 4370–4378.
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7186-01-FH02-ESERI-16-05416.pdf
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12879 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=12879 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 12 1.6 Adobe Acrobat Pro DC 20.6.20042 2024-08-27 14:24:58 7186-01-FH02-ESERI-16-05416.pdf UniSZA Private Access Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis Journal of Testing and Evaluation This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-APILNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2 , root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-APIDOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 1,191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency. 44 1 376-384 [1] Azid, A., Juahir, H., Latif, M. T., Zain, S. M., and Osman, M. R., “Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia,” J. Environ. Prot., Vol. 4, No. 12, 2013, pp. 1–10. [2] Dominick, D., Juahir, H., Latif, M. T., Zain, S. M., and Aris, A. Z., “Spatial Assessment of Air Quality Patterns in Malaysia Using Multivariate Analysis,” Atmos. Environ., Vol. 60, 2012, pp. 172–181. [3] Mutalib, S. N. S. A., Juahir, H., Azid, A., Sharif, S. M., Latif, M. T., Aris, A. Z., Zain, S. M., and Dominick, D., “Spatial and Temporal Air Quality Pattern Recognition Using Environmetric Techniques: A Case Study in Malaysia,” Environ. Sci. Process Impacts, Vol. 15, No. 9, 2013, pp. 1717–1728. [4] Abdullah, A. M., Samah, M. A. A., and Tham, Y. J., “An Overview of the Air Pollution Trend in Klang Valley, Malaysia,” Open Environ. Sci., Vol. 6, 2012, pp. 13–19. [5] Afroz, R., Hassan, M. N., and Ibrahim, N. A., 2003, “Review of Air Pollution and Health Impacts in Malaysia,” Environ. Res., Vol. 92, No. 2, pp. 71–77. [6] Azmi, S. Z., Latif, M. T., Ismail, A. S., Juneng, L., and Jemain, A. A., 2010, “Trend and Status of Air Quality at Three Different Monitoring Stations in the Klang Valley, Malaysia,” Air Qual. Atmos. Health, Vol. 3, No. 1, pp. 53–64. [7] Azid, A., Juahir, H., Aris, A. Z., Toriman, M. E., Latif, M. T., Zain, S. M., Yusof, K. M. K. K., and Saudi, A. S. M., “From Sources to Solution,” Proceedings of the International Conference on Environmental Forensics 2013, A. Z. Aris, T. H. T. Ismail, R. Harun, A. M. Abdullah, and M. Y. Ishak, Eds., Springer, New York, 2014, 307 pp. [8] Wahid, N. B. A., Latif, M. T., and Suratman, S., “Composition and Source Apportionment of Surfactants in Atmospheric Aerosols of Urban and Sub-Urban Areas in Malaysia,” Chemosphere, Vol. 91, No. 11, 2013, pp. 1508–1516. [9] Rai, R., Rajput, M., Agrawal, M., and Agrawal, S. B., “Gaseous Air Pollutants: A Review on Current and Future Trends of Emissions and Impact on Agriculture,” J. Sci. Res., Vol. 55, 2011, pp. 77–102. [10] Balasubramanian, R., Qian, W. B., Decesari, S., Facchini, M. C., and Fuzzi, S., “Comprehensive Characterization of PM2.5 Aerosols in Singapore,” J. Geophys. Res., Vol. 108, No. D16, 2003, p. 4523. [11] Jamal, H. H., Pillay, M. S., Zailina, H., Shamsul, B. S., Sinha, K., Zaman Huri, Z., Khew, S. L., Mazrura, S., Ambu, S., Rahimah, A., and Ruzita, M. S., 2004, “A Study of Health Impact & Risk Assessment of Urban Air Pollution in Klang Valley,” UKM Pakarunding Sdn. Bhd., Kuala Lumpur, Malaysia. [12] Rahman, N. H. A., Lee, M. H., Latif, M. T., and Suhartono, S., “Forecasting of Air Pollution Index With Artificial Neural Network,” J. Teknol., Vol. 63, No. 2, 2013, pp. 59–64. [13] Bishoi, B., Prakash, A., and Jain, V. K., “A Comparative Study of Air Quality Index Based on Factor Analysis and US-EPA Methods for an Urban Environment,” Aero. Air Qual. Res., Vol. 9, No. 1, 2009, pp. 1–17. [14] Department of the Environment, “Air Pollutant Index (API),” 2013, http://www.doe.gov.my/webportal/en/info-umum/englishair-pollutant-index-api/. Accessed on 1 August 2014. [15] Azid, A., Juahir, H., Toriman, M. E., Kamarudin, M. K. A., Saudi, A. S. M., Hasnam, C. N. C., Aziz, N. A. A., Azaman, F., Latif, M. T., Zainuddin, S. F. M., Osman, M. R., and Yamin, M., “Prediction of the Level of Air Pollution Using Principal Component Analysis and Artificial Neural Network Techniques: A Case Study in Malaysia,” Water Air Soil Pollut., Vol. 225, No. 8, 2014, p. 2063. [16] Alkasassbeh, M., Sheta, A. F., Faris, H., and Turabieh, H., “Prediction of PM10 and TSP Air Pollution Parameters Using Artificial Neural Network Autoregressive, External Input Models: A Case Study in Salt, Jordan,” Middle-East J. Sci. Res., Vol. 14, No. 7, 2013, pp. 999–1009. [17] Suh, S. C., Practical Application of Data Mining, Jones & Bartlett Learning, Sudbury, MA, 2012. [18] Xie, H., Ma, F., and Bai, Q., “Prediction of Indoor Air Quality Using Artificial Neural Networks,” 2009 Fifth International Conference on Natural Computation, Tianjin, China, August 14–16, 2009, IEEE Computer Society, Washington, DC, pp. 414–418. [19] Karatzas, K. and Kaltsatos, S., “Air Pollution Modelling With the Aid of Computational Intelligence Methods in Thessaloniki, Greece,” Simulation Modelling Practice and Theory, Vol. 15, No. 10, 2007, pp. 1310–1319. [20] Palani, S., Tkalich, P., Balasubramanian, R., and Palanichamy, J., “ANN Application Prediction of Atmospheric Nitrogen Deposition to Aquatic Ecosystems,” Marine Pollut. Bull., Vol. 62, No. 6, 2011, pp. 1198–1206. [21] Zhang, G., Eddy Patuwo, B., and Hu, M. Y., “Forecasting With Artificial Neural Networks: The State of the Art,” Int. J. Forecast., Vol. 14, No. 1, 1998, pp. 35–62. [22] Kurt, A., Gulbagai, B., Karaca, F., and Alagha, O., “An Online Air Pollution Forecasting System Using Neural Networks,” Environ. Int., Vol. 34, No. 5, 2008, pp. 592–598. [23] Afzali, M., Afzali, A., and Zahedi, G., “The Potential of Artificial Neural Network Technique in Daily and Monthly Ambient Air Temperature Prediction,” Int. J. Environ. Sci. Dev., Vol. 3, No. 1, 2012, pp. 33–38. [24] Brey, T., Teichmann, A. J., and Borlich, O., “Artificial Neural Network Versus Multiple Linear Regression: Predicting P/B Ratio From Empirical Data,” Marine Ecol. Prog. Ser., Vol. 140, 1996, pp. 251–256. [25] Pao, H. T., “A Comparison of Neural Network and Multiple Linear Regression Analysis in Modelling Capital Structure,” Exp. Syst. Appl., Vol. 35, 2008, pp. 720–727. [26] Zaefizadeh, M., Khayatnezhad, M., and Gholamin, R., “Comparison of Multiple Linear Regression and Artificial Neural Network in Predicting the Yield Using Its Components in the Hulless Barley,” Am.-Eurasian J. Agric. Environ. Sci., Vol. 10, No. 1, 2011, pp. 60–64. [27] Manache, G. and Melching, C. S., “Identification of Reliable Regression- and Correlation-Based Sensitivity Measures for Importance Ranking of Water Quality Model Parameters,” Environ. Mod. Soft., Vol. 23, No. 5, 2008, pp. 549–562. [28] Nasir, M. F. M., Juahir, H., Roslan, N., Mohd, I., Shafie, N. A., and Ramli, N., “Artificial Neural Networks Combined With Sensitivity Analysis as a Prediction Model for Water Quality Index in Juru River, Malaysia,” Int. J. Environ. Prot. Vol. 1, No. 3, 2011, pp. 1–8. [29] Zali, M. A., Retnam, A., Juahir, H., Zain, S. M., Kasim, M. F., Abdullah, B., and Saadudin, S. B., “Sensitivity Analysis for Water Quality Index (WQI) Prediction for Kinta River, Malaysia,” World Appl. Sci. J., Vol. 14, 2011, pp. 60–65. [30] Department of Environment (DOE), “Main Sources of Air Pollution in Malaysia,” http://apims.doe.gov.my/apims/ General%20Info%20of%20Air%20Pollutant%20Index.pdf. Accessed on 1 August 2014. [31] Department of Environment (DOE), A Guide to Air Pollutant Index in Malaysia (API), 3rd ed., DOE, Kuala Lumpur, Malaysia, 1997, 20 pp. [32] Alam Sekitar Malaysia Sdn. Bhd. (ASMA), “Standard Operating Procedure for Continuous Air Quality Monitoring,” Shah Alam, Selangor, Malaysia, 2007. [33] Junninen, H., Niska, H., Tuppurainen, K., Ruuskanen, J., and Kolehmainen, M., “Methods for Imputation of Missing Values in Air Quality Data Set,” Atmos. Environ., Vol. 38, No. 18, 2004, pp. 2895–2907. [34] Gazzaz, N. M., Yusoff, M. K., Aris, A. Z., Juahir, H., and Ramli, M. F., “Artificial Neural Network Modelling of the Quality Index for Kinta River (Malaysia) Using Water Quality Variables as Predictors,” Marine Pollut. Bull., Vol. 64, 2012, pp. 2409–2420. [35] Daliakopoulos, I. N., Coulibaly, P., and Tsanis, I. K., “Groundwater Level Forecasting Using Artificial Neural Networks,” J. Hydrol., Vol. 309, Nos. 1–4, 2005, pp. 229–240. [36] Kisi, O., “Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models,” Turkish J. Eng. Environ. Sci., Vol. 29, 2005, pp. 9–20. [37] Lee, J. H. W., Huang, Y., Dickman, M., and Jayawardena, A. W., “Neural Network Modeling of Coastal Algal Blooms,” Ecol. Model., Vol. 159, Nos. 2–3, 2003, pp. 179–201. [38] Latif, M. T., Azmi, S. Z., Noor, A. D. M., Ismail, A. S., Johny, Z., Idrus, S., Mohamad, A. F., and Mokhtar, M., “The Impact of Urban Growth on Regional Air Quality Surrounding the Langat River Basin, Malaysia,” Environmentalist, Vol. 31, No. 3, 2011, pp. 315–324. [39] Mustafa, M., Syed Abdul Kader, S. Z., and Sufian, A., “Coping With Climate Change Through Air Pollution Control: Some Legal Initiatives From Malaysia,” 2012 International Conference on Environment, Energy and Biotechnology, Kuala Lumpur, Malaysia, May 5–6, 2012, Vol. 33, pp. 101–105. [40] Atkinson, R., “Atmospheric Chemistry of VOCs and NOx,” Atmos. Environ., Vol. 34, 2000, pp. 2063–2101. [41] Juneng, L., Latif, M. T., Tangang, F. T., and Mansor, H., “Spatio-Temporal Characteristics of PM10 Concentration Across Malaysia,” Atmos. Environ., Vol. 43, No. 30, 2009, pp. 4584–4594. [42] Juneng, L., Latif, M. T., and Tangang, F., “Factors Influencing the Variations of PM10 Aerosol Dust in Klang Valley, Malaysia During the Summer,” Atmos. Environ., Vol. 45, No. 26, 2011, pp. 4370–4378.
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Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| summary |
This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-APILNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2 , root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-APIDOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 1,191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency.
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| title |
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| title_full |
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| title_fullStr |
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| title_full_unstemmed |
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| title_short |
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis
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| title_sort |
selection of the most significant variables of air pollutants using sensitivity analysis
|