Bird behaviour characterisation and environment dependence modelling in airport airspace based on radar datasets

Abstract Bird strike accidents are critical threats for aviation safety especially in airport airspaces. Environment friendly solutions are preferred for wildlife managements to achieve harmonic coexistence between airports and surrounding environments. Avian radar systems are the most effective remote sensing approach for long-range and all-weather birds monitoring. Massive historical avian radar datasets and other data sources provide an opportunity to explore relevance between bird behaviour and environments. This paper proposes a bird behaviour characterisation and prediction method to reveal bird behaviour dependency with weather parameters. Bird behaviours are modelled as indices and grades from selected avian radar datasets. Weather dependence are studied from single parameter to multivariable parameters. The random forest model is selected as a behaviour grade prediction model taking four weather parameters as system inputs. Radar datasets for diurnal and nocturnal birds are constructed to validate their behaviour characters and prediction performance, respectively. Experiment results verify the feasibility of bird behaviour prediction using weather parameters, but also reflect some insufficiencies within the proposed method. Data sufficiency and severe weather considerations are also discussed to analyse their impact on prediction accuracy. A more comprehensive prediction model with standardised avian radar data quality and enhanced weather information accuracy is promising to further elevate the application significance of the proposed method.