Applying Neural Networks and Geographical Information Systems to Airport Noise Evaluation

  • Yingjie Yang
  • David Gillingwater
  • Chris Hinde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


The assessment of aircraft noise is becoming an increasingly important task in ensuring sustainable airport development. Aircraft noise is influenced by many complex factors and traditional laboratory models are not sufficient to assess the exposure to noisy flights of specific local communities in proximity to an airport. In this paper neural network and fuzzy set methods have been integrated with Geographical Information Systems to provide an alternative method to evaluate airport noise.


Neural Network Geographical Information System Fuzzy Membership Aircraft Noise Aircraft Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Morrell, P., Lu, C.H.: Aircraft Noise Social Cost and Charge Mechanisms - A Case Study of Amsterdam Airport Schiphol. Transportation Research, Part D: Transport and Environment 5, 305–320 (2000)CrossRefGoogle Scholar
  2. 2.
    Connor, T.S.: Integrated Noise Model - The Federal Aviation Administration’s Computer Program for Predicting Noise Exposure Around an Airport. In: Proceedings of the International Conference on Noise Engineering. INTER-NOISE80, pp. 127–130 (1980)Google Scholar
  3. 3.
    Yang, Y., Gillingwater, D., Hinde, C.: An Intelligent System for the Sustainable Development of Airports. In: Proceedings of the 9th World Conference on Transportation Research, WCTR, F5-02 (2001b)Google Scholar
  4. 4.
    Yang, Y., Gillingwater, D., Hinde, C., Upham, P.: A Scaled Approach to Developing a Decision Support System for Sustainable Airport Development. In: Proceedings of the UK Sustainable Cities and Aviation Network (SCAN-UK) Conference, Manchester (2001d)Google Scholar
  5. 5.
    Hechi-Nielson, R.: Neurocomputing. Addison-Welsley, Reading (1990)Google Scholar
  6. 6.
    Zadeh, L.: Fuzzy Sets. Information and Control, 8 338, 338–353 (1965)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Burrough, P.A., McDonnell, A.M.: Principles of Geographical Information Systems. Oxford University Press, Oxford (1998)Google Scholar
  8. 8.
    Rigol, J.P., Jarvis, C.H., Stuart, N.: Artificial Neural Networks as a Spatial Interpolation. International Journal of Geographical Information Science 15, 323–343 (2001)CrossRefGoogle Scholar
  9. 9.
    Yang, Y., Rosenbaum, M.: Spatial Data Analysis with ANN: Modelling to Manage Geoenvironmental Problems Caused by Harbour Siltation. In: Proceedings of International Symposium on Spatial Data Quality (ISSDQ 1999), pp. 534–541 (1999)Google Scholar
  10. 10.
    Yang, Y., Rosenbaum, M.: Artificial Neural Networks Linked to GIS for Determining Sedimentology in Harbours. Journal of Petroleum Science and Engineering 29, 213–220 (2001)CrossRefGoogle Scholar
  11. 11.
    Yang, Y., Rosenbaum, M.: Artificial Neural Networks Linked to GIS. In: Nikravesh, M., Aminzadeh, F., Zadeh, L.A. (eds.) Developments in Petroleum Science, 51: Soft Computing and Intelligent Data Analysis in Oil Exploration, Elsevier Science, Amsterdam (2002)Google Scholar
  12. 12.
    Yang, Y., Rosenbaum, M., Burton, C.: An Intelligent Database for Managing Geoenvironmental Change within Harbours. Environmental Geology 40, 1224–1231 (2001c)CrossRefGoogle Scholar
  13. 13.
    Brunsdon, C., Openshaw, S.: Error Simulation in Vector GIS Using Neural Computing Methods. In: Worboys, M.F. (ed.) Innovation in GIS, Taylor & Francis, London (1994)Google Scholar
  14. 14.
    Fonte, C., Lodwick, W.: Areas of Fuzzy Geographical Entities. International Journal of Geographical Information Science 18, 127–150 (2004)CrossRefGoogle Scholar
  15. 15.
    Rosenfeld, A.: The Diameter of a Fuzzy Set. Fuzzy Sets and Systems 13, 241–246 (1984)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Liu, S., Guo, T., Dang, Y.: Grey System Theory and Its Application. The Science Press of China, Beijing (2000)Google Scholar
  17. 17.
    Yang, Y., Hinde, C., Gillingwater, D.: A New Method to Evaluate a Trained Artificial Neural Network. In: Proceedings of the International Joint Conference on Neural Networks 2001 (IJCNN 2001), pp. 2620–2625 (2001a)Google Scholar
  18. 18.
    Yang, Y., Hinde, C., Gillingwater, D.: A New Method in Explaining Neural Network Reasoning. In: Proceedings of the International Joint Conference on Neural Networks 2003 (IJCNN 2003), pp. 3256–3260 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yingjie Yang
    • 1
  • David Gillingwater
    • 2
  • Chris Hinde
    • 3
  1. 1.Centre for Computational IntelligenceDe Montfort UniversityLeicesterUK
  2. 2.Transport Studies Group, Department of Civil and Building EngineeringLoughborough UniversityLoughboroughUK
  3. 3.Department of Computer ScienceLoughborough UniversityLoughboroughUK

Personalised recommendations