Runway Incursion Event Forecast Model based on LS-SVR with Multi-kernel

Forecasting of runway incursion event is very significant to guide the job of civil aviation safety management. It is an important part of the runway incursion early warning management. However, prediction of runway incursion event is a complicated problem due to its non-linearity and the small quantity of training data. As a novel type of learning machine, support vector machine (SVM) has been gaining popularity due to their promising performance, such as dealing with the data of small sample, the high dimension and the excellent generalization ability. However, the generalization ability of SVM often relies on whether the selected kernel function is suitable for real data. To lessen the sensitivity of different kernels and improve generalization ability, least square support vector regression (LS-SVR) with multi-kernel is proposed to forecast the runway incursion event in this paper. The two experimental results indicate that LS-SVR with multi-kernel model is better than LS-SVR with individual kernel model and generalized regression neural network (GRNN) model. Consequently, multi-kernel LS-SVR model is a proper alternative for forecasting of the runway incursion event.