Intelligent decision making for airport bird-repelling with support vector machine

To impove the management of bird-strike avoidance at airport and realize the linkage of avian radar with multiple bird-repelling devices, an intelligent decision making method was proposed for airport bird-repelling based on support vector machine (SVM). The method includes two steps of training and testing. In the training step, the bird-repelling strategy classification model was established by data pretreatment and SVM training, which are combined with expert knowledge and large amount of historical bird information collected by the airport linkage system for bird detection, surveillance and repelling. In the testing step, the bird-repelling strategy classification model was continuously corrected and optimized according to the real-time intelligent bird-repelling strategy results. Through the real bird information data and several bird-repelling examples of a certain airport, it is demonstrated that the decision accuracy of bird-repelling strategy classification model is relatively high, and it can solve new problems by self correction and optimization. The proposed method achieves the optimized combination of multiple bird-repelling devices against real-time bird information with great improvement of bird-repelling effect, overcoming the tolerance of birds to the bird-repelling devices due to their long-term repeated operation.