Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets

نویسندگان

چکیده

Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods required for the harmonious coexistence of management and friendly ecology. Existing works indicate close relationship between bird activities weather. The relevance activity weather is favorable intuitive understanding ecological environments providing constructive references. This paper introduces characterization forecasting method based on information. modeled quantified into different grades. Their with parameters first explored independently support multivariable study. Two groups machine learning strategies adopted test their feasibility prediction. Radar datasets from diurnal nocturnal study areas constructed an avian radar system deployed at airport. Experimental results verify that both could achieve information acceptable accuracy. random forest model better choice its robustness adjustability feature inconsistencies. Weather deviation airspace ground measurement factor limiting prediction data sufficiency dependency discussed. reasonability proposed modeling method; more improvements accuracy necessary further elevate application significance model.

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ژورنال

عنوان ژورنال: Aerospace

سال: 2023

ISSN: ['2226-4310']

DOI: https://doi.org/10.3390/aerospace10050462