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The number of collisions between aircraft and birds in the airspace has beenincreasing at an alarming rate over the past decade due to increasing birdpopulation, air traffic and usage of quieter aircraft. Bird strikes withaircraft are anticipated to increase dramatically when emerging Advanced AirMobility aircraft start operating in the low altitude airspace whereprobability of bird strikes is the highest. Not only do such bird strikes canresult in human and bird fatalities, but they also cost the aviation industrymillions of dollars in damages to aircraft annually. To better understand thecauses and effects of bird strikes, research to date has mainly focused onanalyzing factors which increase the probability of bird strikes, identifyinghigh risk birds in different locations, predicting the future number of birdstrike incidents, and estimating cost of bird strike damages. However, researchon bird movement prediction for use in flight planning algorithms to minimizethe probability of bird strikes is very limited. To address this gap inresearch, we implement four different types of Long Short-Term Memory (LSTM)models to predict bird movement latitudes and longitudes. A publicly availabledata set on the movement of pigeons is utilized to train the models andevaluate their performances. Using the bird flight track predictions, aircraftdepartures from Cleveland Hopkins airport are simulated to be delayed byvarying amounts to avoid potential bird strikes with aircraft during takeoff.Results demonstrate that the LSTM models can predict bird movement with highaccuracy, achieving a Mean Absolute Error of less than 100 meters,outperforming linear and nonlinear regression models. Our findings indicatethat incorporating bird movement prediction into flight planning can be highlybeneficial.

Bird Movement Prediction Using Long Short-Term Memory Networks to Prevent Bird Strikes with Low Altitude Aircraft