| Citation: | CHEN B，LIU Y，YIN K L，et al. Runway temperature data mechanism joint prediction based on LSTM under ice and snow[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2184-2194 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0579
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Runway temperature is an important factor in runway icing. Fully considering the transient characteristics of the temperature mechanism model and the time sequence of temperature multivariate time series data, the paper has developed a joint model based on the long short term memory (LSTM) neural network and temperature mechanism model. Firstly, the influencing elements with a greater correlation with runway temperature were selected by the study using the maximum information coefficient approach to serve as the model’s input. Secondly, the paper uses the dynamic time warping method to cluster temperature data under different snowfall conditions, and then develops an LSTM model adapted to different snowfall or icing situations. Finally, to solve the disadvantage of LSTM which can not be characterized by the runway parameters that change irregularly and frequently, the paper developed a joint model based on the LSTM neural network and temperature mechanism model by using the minimum error method. The joint model’s degree of accuracy is 99.34%, which is superior than both the data model and the mechanism model, when the prediction time step is 20 minutes and the residual threshold is ±0.5°C, according to the simulation’s result based on the ice and snowfall weather condition data. With the same condition, the joint model has better accuracy than the BP model, the regression model and the support vector machine model. Average accuracy increased by 26.11%. It proved the joint model based on the LSTM neural network and temperature mechanism model has better accuracy according to the transient characteristics of the mechanism model and the periodic time sequence of the multivariate time series of pavement temperature.
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