Estimating wildlife strike costs at US airports: A machine learning approach

Current lower bound estimates of the economic burden of wildlife strikes make use of mean cost assignment to impute missing values in the National Wildlife Strike Database (NWSD). The accuracy of these estimates, however, are undermined by the skewed nature of reported cost data and fail to account for differences in observed strike characteristics-e.g., type of aircraft, size of aircraft, type of damage, size of animal struck, etc. This paper makes use of modern machine learning techniques to provide a more accurate measure of the strike-related costs that accrue to the US civil aviation industry. We estimate that wildlife strikes costed the US civil aviation industry a minimum average of $54.3 million in total losses annually over the 1990-2018 period. If one assumes that wildlife strikes were underreported by as much as a factor of 3 over the same period, our estimates still fall below previous lower bound estimates.

Automated summary

The study utilizes modern machine learning techniques to provide a more accurate estimate of the economic burden of wildlife strikes on the US civil aviation industry, revealing an average annual loss of approximately $54.3 million. Further research should consider differences in strike characteristics for more comprehensive findings.