Abstract
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.
Abstract (translated)
过去十年里，由于鸟类数量的增加、空勤和飞机使用的安静型飞机越来越多，空中撞击航空器与鸟类之间的碰撞数量不断增加。预计，当低空空域中出现新型先进空运工具时，预计鸟类撞击航空器的显著增加。不仅这些鸟类撞击会导致人类和鸟类死亡，而且它们每年还会给航空业造成数百万美元的损失。为了更好地了解撞击的原因和影响，迄今为止，研究主要集中在分析增加鸟类撞击概率的因素、确定不同地点的高风险鸟类、预测未来的鸟类撞击事件以及估计鸟类撞击损失成本。然而，关于用于飞行计划算法预测鸟类移动的研究却非常有限。为了填补这一研究空白，我们采用了四种不同的长短时记忆（LSTM）模型来预测鸟的移动纬度和经度。一个可公开获取的鸽子运动数据集用于训练模型并评估其性能。利用鸟类飞行轨迹预测，我们将克利夫兰霍金斯机场的飞机出发时间模拟为由于避免与鸟类撞击而推迟，数量 varying。结果表明，LSTM模型可以预测鸟类运动，具有较高的准确度，实现平均绝对误差小于100米，优于线性和非线性回归模型。我们的研究结果表明，将鸟类运动预测纳入飞行计划可以带来极大的好处。
URL
https://arxiv.org/abs/2312.12461
