Bird Movement Prediction Using Long Short-Term Memory Networks to
  Prevent Bird Strikes with Low Altitude Aircraft
Elaheh Sabziyan Varnousfaderani Syed A. M. Shihab
The number of collisions between aircraft and birds in the airspace has been
increasing at an alarming rate over the past decade due to increasing bird
population, air traffic and usage of quieter aircraft. Bird strikes with
aircraft are anticipated to increase dramatically when emerging Advanced Air
Mobility aircraft start operating in the low altitude airspace where
probability of bird strikes is the highest. Not only do such bird strikes can
result in human and bird fatalities, but they also cost the aviation industry
millions of dollars in damages to aircraft annually. To better understand the
causes and effects of bird strikes, research to date has mainly focused on
analyzing factors which increase the probability of bird strikes, identifying
high risk birds in different locations, predicting the future number of bird
strike incidents, and estimating cost of bird strike damages. However, research
on bird movement prediction for use in flight planning algorithms to minimize
the probability of bird strikes is very limited. To address this gap in
research, we implement four different types of Long Short-Term Memory (LSTM)
models to predict bird movement latitudes and longitudes. A publicly available
data set on the movement of pigeons is utilized to train the models and
evaluate their performances. Using the bird flight track predictions, aircraft
departures from Cleveland Hopkins airport are simulated to be delayed by
varying amounts to avoid potential bird strikes with aircraft during takeoff.
Results demonstrate that the LSTM models can predict bird movement with high
accuracy, achieving a Mean Absolute Error of less than 100 meters,
outperforming linear and nonlinear regression models. Our findings indicate
that incorporating bird movement prediction into flight planning can be highly
beneficial.