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

• Published in: Aeronautical journal Vol. 122; no. 1252; pp. 988 - 1002
• Main Authors: Chen, Weishi, Zhang, Jie, Li, Jing
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.06.2018
• Subjects: Aerospace engineering; Aircraft; Airport planning; Airports; Architectural engineering; Aviation; Bird impact; Bird strike tests; Decision making; Exponential functions; Interdisciplinary subjects; Light; R&D; Research & development; Risk assessment; Risk levels; Sensors; Support vector machines; Target recognition; United States > US; China; risk assessment; Support vector machine; classification; decision-making; bird-strike
• ISSN: 0001-9240; 2059-6464
• DOI: 10.1017/aer.2018.45

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.