Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-17T14:30:12.840Z Has data issue: false hasContentIssue false
  • English
  • Français

Intelligent decision-making with bird-strike risk assessment for airport bird repellent

Published online by Cambridge University Press:  08 May 2018

Weishi Chen ,
Jie Zhang  and
Jing Li
Weishi Chen*
Affiliation:
China Academy of Civil Aviation Science and Technology, Airport Research Institute, Beijing, China
Jie Zhang
Affiliation:
China Academy of Civil Aviation Science and Technology, Airport Research Institute, Beijing, China
Jing Li
Affiliation:
China Academy of Civil Aviation Science and Technology, Airport Research Institute, Beijing, China
*
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

An intelligent decision-making method was proposed for airport bird-repelling based on a Support Vector Machine (SVM) and bird-strike risk assessment. The bird-strike risk assessment model is established with two exponential functions to separate the risk levels, while the SVM method includes two steps of training and testing. After the risk assessment, the Bird-Repelling Strategy Classification Model (BRSCM) was trained based on the expert knowledge and large amount of historical bird information collected by the airport linkage system for bird detection, surveillance and repelling. Then, in the testing step, the BRSCM was continuously optimised according to the real-time intelligent bird-repelling strategy results. Through several bird-repelling examples of a certain airport, it is demonstrated that the decision accuracy of BRSCM is relatively high, and it could solve new problems by self-correction. The proposed method achieved the optimised operation of multiple bird-repelling devices against real-time bird information with great improvement of bird-repelling effects, overcoming the tolerance of birds to the bird-repelling devices due to their long-term repeated operation.

Keywords

Support vector machinerisk assessmentbird-strikeclassificationdecision-making
Type
Research Article
Information
The Aeronautical Journal , Volume 122 , Issue 1252 , June 2018 , pp. 988 - 1002
Copyright
Copyright © Royal Aeronautical Society 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Beason, R.C., Nohara, T.J. and Weber, P. Beware the Boojum: Caveats and strengths of avian radar, Human-Wildlife Interactions, January 2013, 7, (1), pp 16-46.Google Scholar
2. O'donnell, J.M. FAA advisory circular on reporting wildlife aircraft strikes, AC 150/5200-32B, 2013. Available from: https://www.faa.gov/airports/resources/advisory_circulars/index.cfm/go/document.current/documentNumber/150_5200-32Google Scholar
3. Li, Y.L. and Shi, X.P. Investigation of the present status of research on bird impacting on commercial airplanes. Acta Aeronautica et Astronautica Sinica, February 2012, 33, (2), pp 189-198.Google Scholar
4. Zhang, J. 2015’s Annual report on bird-strike information analysis of China civil aviation, CAST No 001, 2016. Available from: http://www.birdstrike.cn/webcolumn/displayInfo.action?webContentId=0a33139e03a647ef9db532f12ed8b023Google Scholar
5. Liu, Z.Q., Chen, S.Y., Shao, Z.Z. et al. The design and implementation of bird driving system by the airport runway, J Shandong Normal University (Natural Science), January 2017, 32, (1), pp 66-69.Google Scholar
6. Nohara, T.J., Beason, R.C. and Clifford, S.P. The role of radar-activated waterfowl deterrents on tailings ponds, The International Oil Sands Tailings Conference, December 2012, Edmonton, Alberta, pp 1-6.Google Scholar
7. Anderson, R. Avian radar systems, DeTect No 001, 2007. Available from: http://detect-inc.com/aircraft-birdstrike-avoidance-radar/Google Scholar
8. Nohara, T.J. and Unkrainec, W.A. An overview of avian radar developments–Past, present and future, Bird Strike North American Conference, 2007, Kingston, Canada, pp 1-10.Google Scholar
9. Robin. Robin Systems&Services. Robin No 001, 2011. Available from: https://www.robinradar.com/downloads/Google Scholar
10. Chen, W.S. and Li, J. Review on development and applications of avian radar technology, Modern Radar, February 2017, 39, (2), pp 7-17.Google Scholar
11. Chen, W.S., Ning, H.S. and Li, J. Flying bird detection and hazard assessment for avian radar system, J Aerospace Engineering, March 2012, 25, (2), pp 246-255.CrossRefGoogle Scholar
12. Ning, H.S. and Chen, W.S. Bird strike risk evaluation at airports, Aircraft Engineering and Aerospace Technology, February 2014, 86, (2), pp 129-137.CrossRefGoogle Scholar
13. Han, J.W., Kamber, M. and Pei, J. Data Mining: Concepts and Techniques, 3rd ed, 2011, Morgan Kaufmann, San Francisco, California, US.Google Scholar
14. Vapnik, V.N. Statistical Learning Theory, 1998, Wiley, New York, USA.Google Scholar
15. Ma, Y.F., Liang, X. and Zhou, X.P. A fast sparse algorithm for least squares support vector machine based on global representative points, Acta Automatica Sinca, January 2017, 43, (1), pp 132-141.Google Scholar