Artificial Neural Network Modeling of Healthy Risk Level Induced by Aircraft Pollutant Impacts around Soekarno Hatta International Airport


Aircraft pollutant emissions are an important part of sources of pollution that directly or indirectly affect human health and ecosystems. This research suggests an Artificial Neural Network model to determine the healthy risk level around Soekarno Hatta International Airport-Cengkareng Indonesia. This ANN modeling is a flexible method, which enables to recognize highly complex non-linear correlations. The network was trained with real measurement data and updated with new measurements, enhancing its quality and making it the ideal method for this research. Measurements of aircraft pollutant emissions are carried out with the aim to be used as input data and to validate the developed model. The obtained results concerned the improved ANN architecture model based on pollutant emissions as input variables. ANN model processes variables—hidden layers—and gives an output variable corresponding to a healthy risk level. This model is characterized by a 4-10-1 scheme. Based on ANN criteria, the best validation performance is achieved at epoch 28 from 34 epochs with the Mean Squared Error (MSE) of 9 × 10-3. The correlation between targets and outputs is confirmed. It validated a close relationship between targets and outputs. The network output errors value approaches zero. Further research is needed with the aim to enlarge the scheme of the ANN model by increasing its input variables. This is one of the major key defining environmental capacities of an airport that should be applied by Indonesian airport authorities. These would institute policies to manage or reduce pollutant emissions considering population and income growth to be socially positive.

Share and Cite:

S. Khardi, J. Kurniawan, I. Katili and S. Moersidik, "Artificial Neural Network Modeling of Healthy Risk Level Induced by Aircraft Pollutant Impacts around Soekarno Hatta International Airport," Journal of Environmental Protection, Vol. 4 No. 8A, 2013, pp. 28-39. doi: 10.4236/jep.2013.48A1005.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] International Civil Aviation Organization (ICAO), “Airport Local Air Quality Guidance Manual,” 2007.
[2] International Civil Aviation Organization (ICAO), “Environmental Report,” 2007.
[3] European Environmental Agency (EEA), “Emission Inventory Guidebook,” 2009.
[4] Federal Aviation Administration (FAA), “Office Environment and Energy, Aviation & Emissions A Primer,” 2005.
[5] M. Dameris, V. Grewe, I. Kohler, P. Sausen, C. Bruehl, J. Grooss and B. Steil, “Impact of Aircraft NOx Emissions on Tropospheric and Stratospheric Ozone, Part II: 3-D Model Results,” Atmospheric Environment, Vol. 32, No. 18, 1998, pp. 3185-3199. doi:10.1016/S1352-2310(97)00505-0
[6] B. E. Anderson, C. Gao and D. R. Blake, “Hydrocarbon Emissions from a Modern Commercial Airliner,” Atmospheric Environment, Vol. 40, No. 19, 2006, pp. 3601-3612. doi:10.1016/j.atmosenv.2005.09.072
[7] J. S. Kurniawan and S. Khardi, “Comparison of Methodologies Estimating Emissions of Aircraft Pollutants, Environmental Impact Assessment around Airports,” Environmental Impact Assessment Review, Vol. 31, No. 3, 2011, pp. 240-252. doi:10.1016/j.eiar.2010.09.001
[8] Federal Aviation Administration (FAA), “Air Quality Procedures for Civilian Airports and Air Force Bases, Appendix D: Aircraft Emission Methodology,” 1997.
[9] M. Gauss, I. S. A. Isaksen, D. S. Lee and O. A. Sovde, “Impact of Aircraft NOx Emissions on the Atmosphere— Tradeoffs to Reduce the Impact,” Atmospheric Chemistry and Physics, Vol. 5, 2005, pp. 12255-12311. doi:10.5194/acpd-5-12255-2005
[10] Federal Aviation Administration (FAA), “Emission Dispersion Modeling System,” 2009.
[11] International Civil Aviation Organization (ICAO), “International Standards and Recommended Practices,” Environmental Protection Annex 16, Volume II Aircraft Engine Emissions, 2nd Edition, ICAO, Montréal, 1993.
[12] International Civil Aviation Organization (ICAO), “Aircraft-Operations-Flight-Procedures,” Doc-8168-Vol. 1, 5th Edition, ICAO, Montréal, 2006.
[13] International Civil Aviation Organization (ICAO), “Annex-16-Vol-2-3rd-Edition, Aircraft Engine Emissions,” 2008. ards_FR.aspx
[14] J. G. J. Olivier, “Inventory of Aircraft Emissions: A Review of Recent Literature,” Report No. 736 301 008, National Institute of Public Health and Environmental Protection, Bilthoven, 1991
[15] S. Khardi, “Development of Innovative Optimized Flight Paths of Aircraft Takeoffs Reducing Noise and Fuel Consumption,” Acta Acustica United with Acustica, Vol. 97, No. 1, 2011, pp. 148-154.
[16] S. Khardi and L. Abdallah, “Optimization Approaches of Aircraft Flight Path Reducing Noise: Comparison of Modeling Methods,” Applied Acoustics, Vol. 73, No. 4, 2012, pp. 291-301. doi:10.1016/j.apacoust.2011.06.012
[17] ALAQS, “Airport Local Air Quality Studies,” 2009.
[18] Sourdine II WP5, “Airport Noise and Emission Modeling Methodology,” 2005.
[19] Environmental Protection Agency (EPA), “Evaluation of Air Pollutant Emissions from Subsonic Commercial Jet Aircraft,” 1999.
[20] S. Baughcun, et al., “Scheduled Aircraft Emission Inventories for 1992. Database Development and Analysis,” NASA Contract Report No. 4700, NASA Langley Research Centre, Washington DC, 1996.
[21] A. Cochocki and R. Unbehauen, “Neural Networks for Optimization and Signal Processing,” John Wiley & Sons, Inc., New York, 1993.
[22] M. Gevrey, I. Dimopoulos and S. Lek, “Review and Comparison of Methods to Study the Contribution of Variables in Artificial Neural Network Models,” Ecological Modeling, Vol. 160, No. 3, 2003, pp. 249-264. doi:10.1016/S0304-3800(02)00257-0
[23] P. D. Wasserman, “Advanced Methods in Neural Computing,” John Wiley & Sons, Inc., New York, 1993.
[24] Z. H. Zhou, J. Wu and W. Tang, “Ensembling Neural Networks: Many Could Be Better than All,” Artificial Intelligence, Vol. 137, No. 1-2, 2002, pp. 239-263.
[25] H. White, “Artificial Neural Networks: Approximation and Learning Theory,” Blackwell Publishers, Inc., Cambridge, 1992.
[26] B. D. Ripley, “Pattern Recognition and Neural Networks,” Cambridge University Press, Cambridge, 1996.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.