IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i21p8920-d435466.html
   My bibliography  Save this article

Raptor Feeding Characterization and Dynamic System Simulation Applied to Airport Falconry

Author

Listed:
  • José Luis Roca-González

    (Department of Engineering and Applied Technologies, University Centre of Defence at the Spanish Air Force Academy, 30720 Santiago de la Ribera, Spain)

  • Antonio Juan Briones Peñalver

    (Department of Business Economics, Universidad Politécnica de Cartagena, 30201 Cartagena, Spain)

  • Francisco Campuzano-Bolarín

    (Department of Business Economics, Universidad Politécnica de Cartagena, 30201 Cartagena, Spain)

Abstract

Airport falconry is a highly effective technique for reducing wildlife strikes on aircraft, which cause great economic losses. As an example, nowadays, wildlife strikes on aircrafts in the air transport industry are estimated to cost between USD 187 and 937 million in the US and USD 1.2 billion worldwide every year. Moreover, the life-threatening danger that wildlife strikes pose to passengers has prompted security stakeholders to develop countermeasures to prevent wildlife impacts near airport transit zones. The experience acquired from international countermeasure analysis reveals that falconry is the most effective technique to create sustainable wildlife exclusion areas. However, its application in airport environments continues to be regarded as an art rather than a technique; falconers modulate raptors’ behavior by using a trial-and-error system of controlling their hunger to stimulate the need for prey. This paper focuses on a case study where such a decision-making process was designed as a dynamic system applied to feeding planning for raptors that can be used to set an efficient baseline to optimize raptor responses without damaging existing wildlife. The results were validated by comparing the outputs of the model and the falconer’s trial-and-error system, which revealed that the proposed model was 58.15% more precise.

Suggested Citation

  • José Luis Roca-González & Antonio Juan Briones Peñalver & Francisco Campuzano-Bolarín, 2020. "Raptor Feeding Characterization and Dynamic System Simulation Applied to Airport Falconry," Sustainability, MDPI, vol. 12(21), pages 1-21, October.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8920-:d:435466
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/21/8920/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/21/8920/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hesse, Gayle & Rea, Roy V. & Booth, Annie L., 2010. "Wildlife management practices at western Canadian airports," Journal of Air Transport Management, Elsevier, vol. 16(4), pages 185-190.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    2. Li, Chen & Kies, Alexander & Zhou, Kai & Schlott, Markus & Sayed, Omar El & Bilousova, Mariia & Stöcker, Horst, 2024. "Optimal Power Flow in a highly renewable power system based on attention neural networks," Applied Energy, Elsevier, vol. 359(C).
    3. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    4. Arash YoosefDoost & William David Lubitz, 2021. "Archimedes Screw Design: An Analytical Model for Rapid Estimation of Archimedes Screw Geometry," Energies, MDPI, vol. 14(22), pages 1-14, November.
    5. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    6. Hu, Xincheng & Banks, Jonathan & Guo, Yunting & Liu, Wei Victor, 2022. "Utilizing geothermal energy from enhanced geothermal systems as a heat source for oil sands separation: A numerical evaluation," Energy, Elsevier, vol. 238(PA).
    7. Vasile Brătian & Ana-Maria Acu & Camelia Oprean-Stan & Emil Dinga & Gabriela-Mariana Ionescu, 2021. "Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    8. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    9. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    10. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
    11. Andrea Petroselli & Jacek Florek & Dariusz Młyński & Leszek Książek & Andrzej Wałęga, 2020. "New Insights on Flood Mapping Procedure: Two Case Studies in Poland," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    12. Jose Manuel Barrera & Alejandro Reina & Alejandro Maté & Juan Carlos Trujillo, 2020. "Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    13. Fu, Qiang & Wang, Nuo & Shen, MingQi & Song, NanQi & Yan, HuaKun, 2016. "A study of the site selection of a civil airport based on the risk of bird strikes: The case of Dalian, China," Journal of Air Transport Management, Elsevier, vol. 54(C), pages 17-30.
    14. Corey Ducharme & Bruno Agard & Martin Trépanier, 2024. "Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1661-1681, August.
    15. Bhatia, Kushagra & Mittal, Rajat & Varanasi, Jyothi & Tripathi, M.M., 2021. "An ensemble approach for electricity price forecasting in markets with renewable energy resources," Utilities Policy, Elsevier, vol. 70(C).
    16. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    17. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    18. Ahmed Gowida & Tamer Moussa & Salaheldin Elkatatny & Abdulwahab Ali, 2019. "A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    19. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    20. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8920-:d:435466. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.