To prevent aircraft from running off the runway during landing, this paper uses a BP neural network model to predict the aircraft landing distance. In this study, based on the five main influencing factors of airport height, aircraft landing quality, airport runway slope, wind, and ambient temperature, the B737-800 was selected as the reference aircraft and the relevant operational data were collected using Boeing’s LAND software for the study. In addition, this study uses LM (Levenberg–Marquardt) algorithm and GA (genetic algorithm) to optimize the training process, accelerate the computation speed, and improve the shortage of local optimization of BP (back propagation) neural network model and then construct the GA-LM-BP neural network optimization model. Finally, it makes the BP neural network have the ability of global search for optimal solutions. The results show that the predicted landing data are in good agreement with the measured landing data. The maximum absolute error is within 6.66?m and the maximum relative error is within 0.038.
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