DAEN 690 Capstone Project: Wildlife Strike Mitigation Project

Team: AirGuardians
Members: Sai Kumar Aili, Shruty Suman, Vindhya Bodipudi, Tram Pham, Vishnu Priya Karangula
Date: 08 December 2023

Overview

This is a collaborative research initiative between George Mason University students and the Federal Aviation Administration (FAA) addressing the critical challenge of wildlife strikes in aviation. This project leverages advanced data analytics and innovative mitigation strategies to enhance aviation safety and reduce economic losses.

Problem Context and Importance

The problem of wildlife strikes, particularly bird collisions with aircraft, has become increasingly significant in the aviation industry. Here's the context and importance of this issue:

YouTube: How All Passengers Survived the Miracle on the Hudson

Problem Context

• Approximately 272,000 wildlife strikes with civil aircraft were reported in the USA between 1990 and 2022.
• In 2022 alone, about 17,000 strikes were reported at 693 U.S. airports.
• U.S. Air Carriers reported 4,800 strikes at foreign airports between 1990 and 2022.

Importance

• Safety Concerns: Wildlife strikes pose a significant threat to aviation safety, potentially causing damage to aircraft and endangering passengers and crew.
• Increasing Frequency: The number of strikes is rising, especially at altitudes above 152 meters, indicating that current wildlife management strategies at airports are insufficient.
• Global Issue: The problem extends beyond U.S. borders, affecting international flights and airports worldwide.
• Environmental Considerations: Addressing wildlife strikes is crucial not only for aviation safety and economic stability but also for conserving wildlife populations.
• Technological Challenges: The development of quieter aircraft has inadvertently increased the risk of collisions, as wildlife may not detect approaching planes as easily.
• Data-Driven Solutions: The FAA Wildlife Strike Database serves as a critical tool for understanding and mitigating this issue, emphasizing the need for comprehensive data collection and analysis.
• Multifaceted Approach: Addressing this problem requires a combination of habitat management, technological innovations, and community engagement, highlighting its complex nature.

Potential Customers and Business Value

• Airports: To improve safety and reduce incidents
• Airlines: To enhance flight safety and minimize delays
• FAA: Better data for policy-making and regulation
• Wildlife Conservation Groups: To improve wildlife management strategies

Project Architecture

1. Data Collection Layer: FAA Wildlife Strike Database
2. Data Processing Layer: Python for data cleaning and preprocessing
3. Analysis Layer: Machine learning models, statistical analysis tools
4. Visualization Layer: Tableau for heatmaps and interactive dashboards
5. Prediction Layer: AI algorithms for risk assessment and forecasting
6. Reporting Layer: Automated report generation and alert systems

Process

1. Data extraction and cleaning from FAA Wildlife Strike Database
2. Pattern and trend analysis using statistical tools
3. Geospatial analysis with GIS tools
4. Temporal analysis for seasonal variations
5. Visualization creation using Tableau
6. Predictive modeling with machine learning

Results

• Identified high-risk periods and zones for wildlife strikes
• Created heat maps for key airports (e.g., LAX, DFW)
• Developed predictive models for strike damage and costs
• Generated insights on species involvement and effect on flights
• Produced an interactive dashboard for efficient data analysis

Key Objectives

1. Enhance comprehensive data collection
2. Improve habitat management and control
3. Develop and implement mitigation strategies
4. Enhance predictive capabilities using AI and Machine Learning

Tools and Technologies

• Python: Data cleaning and analysis
• Tableau: Heatmap creation and visualization
• Jupyter Notebook: Machine learning for strike prediction
• GIS: Spatial analysis
• AWS: For storage and computing

Requirements

1. Data: Access to FAA Wildlife Strike Database
2. Technical: Python, Tableau, Jupyter Notebook, GIS tools, AWS
3. Functional: Real-time data analysis, predictive modeling, interactive visualization
4. Non-Functional: Scalability, security, user-friendly interfaces
5. Regulatory: Compliance with FAA regulations and data protection laws

Contributors

• Graduate Students: Sai Kumar Aili, Shruty Suman, Vindhya Bodipudi, Tram Pham, Vishnu Priya Karangula
• Professor: Dr. Rajesh Aggarwal, George Mason University
• Federal Aviation Administration

Acknowledgments

Special thanks to the FAA for their support and collaboration on this critical aviation safety project.

Further Reading

• Journal Article: Blackwell, B.F., DeVault, T.L., Seamans, T.W., Lima, S.L., Baumhardt, P. and Fernández-Juricic, E. (2012), Exploiting avian vision with aircraft lighting to reduce bird strikes. Journal of Applied Ecology, 49: 758-766. https://doi.org/10.1111/j.1365-2664.2012.02165.x