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A Study on Machine Learning-Based Estimation of Roadkill Incidents and Exploration of Influencing Factors

  • Journal of Environmental Impact Assessment
  • Abbr : J EIA
  • 2024, 33(2), pp.74-83
  • Publisher : Korean Society Of Environmental Impact Assessment
  • Research Area : Engineering > Environmental Engineering
  • Received : February 15, 2024
  • Accepted : March 4, 2024
  • Published : April 30, 2024

Sojin Heo 1 Jeeyoung Kim 1

1경북대학교

Accredited

ABSTRACT

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.