Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets
Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/10/5/462 |
| _version_ | 1797601616884400128 |
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| author | Qunyu Xu Jia Liu Min Su Weishi Chen |
| author_facet | Qunyu Xu Jia Liu Min Su Weishi Chen |
| author_sort | Qunyu Xu |
| collection | DOAJ |
| description | Bird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model. |
| first_indexed | 2024-03-11T04:02:53Z |
| format | Article |
| id | doaj.art-9f291050b6b34c7086d63984e982025c |
| institution | Directory Open Access Journal |
| issn | 2226-4310 |
| language | English |
| last_indexed | 2024-03-11T04:02:53Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj.art-9f291050b6b34c7086d63984e982025c2023-11-18T00:00:39ZengMDPI AGAerospace2226-43102023-05-0110546210.3390/aerospace10050462Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar DatasetsQunyu Xu0Jia Liu1Min Su2Weishi Chen3Research Institute of Civil Aviation Law, Regulation and Standardization, China Academy of Civil Aviation Science and Technology, Beijing 100028, ChinaResearch Institute for Frontier Science, Beihang University, Beijing 100191, ChinaSchool of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, ChinaAirport Research Institute, China Academy of Civil Aviation Science and Technology, Beijing 100028, ChinaBird strikes are a predominant threat to aviation safety, especially in airport airspace. Effective wildlife surveillance methods are required for the harmonious coexistence of airport management and friendly ecology. Existing works indicate the close relationship between bird activities and weather. The relevance of bird activity and weather is favorable for intuitive understanding of ecological environments and providing constructive wildlife management references. This paper introduces a bird activity characterization and forecasting method based on weather information. Bird activities are modeled and quantified into different activity grades. Their relevance with weather parameters is first explored independently to support the multivariable relevance study. Two groups of machine learning strategies are adopted to test their feasibility for bird activity prediction. Radar datasets from diurnal and nocturnal activity study areas are constructed from an avian radar system deployed at the airport. Experimental results verify that both machine learning strategies could achieve bird activity forecasting based on weather information with acceptable accuracy. The random forest model is a better choice for its robustness and adjustability to feature inconsistencies. Weather information deviation between bird activity airspace and ground measurement is a predominant factor limiting the prediction accuracy. The data sufficiency dependency of the prediction model is discussed. Existing works indicate the reasonability and feasibility of the proposed activity modeling and prediction method; more improvements on weather information accuracy and data sufficiency are necessary to further elevate the application significance of the prediction model.https://www.mdpi.com/2226-4310/10/5/462bird strikeradar remote sensingdata miningmodellingmachine learningwildlife management |
| spellingShingle | Qunyu Xu Jia Liu Min Su Weishi Chen Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets Aerospace bird strike radar remote sensing data mining modelling machine learning wildlife management |
| title | Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets |
| title_full | Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets |
| title_fullStr | Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets |
| title_full_unstemmed | Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets |
| title_short | Quantitative Bird Activity Characterization and Prediction Using Multivariable Weather Parameters and Avian Radar Datasets |
| title_sort | quantitative bird activity characterization and prediction using multivariable weather parameters and avian radar datasets |
| topic | bird strike radar remote sensing data mining modelling machine learning wildlife management |
| url | https://www.mdpi.com/2226-4310/10/5/462 |
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