Short-Term Forecasting Method for Airport Takeoff and Landing Volume Based on SARIMA-LSTM Combination
Short-term forecasting methods according to the demand of air traffic flow management,predict the airport takeoff and landing volume within a 24-hour time span.Firstly,an airport takeoff and landing volume forecasting models based on seasonal auto regressive integrated moving average(SARIMA)and long short term memory network(LSTM)is constructed.Then,with the error re-ciprocal method,the combined forecasting weights are determined to achieve better prediction results.Finally,using Tianjin Binhai Airport as an example to verify the model,the SARIMA(0,1,7)×(0,1,1)24 and LSTM models based on hourly takeoff and landing volume data are built,and a com-bined prediction model with weights of 0.600 and 0.400 respectively is established.The verification re-sults show that the prediction indicator R2 of the combined model reaches 0.904.It demonstrates better prediction performance than back propagation(BP)neural network and other single models.
airport takeoff and landing volumeseasonal auto regressive integrated moving average(SARIMA)modellong short term memory network(LSTM)modelerror reciprocal method
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