Abstract
References
- Yeremieiev, V., Siokhin, V., Matsyura, A. & Bukreev, D. (2024). Statistical methods for processıng data on the impact of wind farms on birds. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 29, 293-304.
Abstract
This article is devoted to the processing and analysis of observational results that make it possible to predict the impact of wind power on the environmental situation; in the course of the work, an algorithm for statistical processing of the results of monitoring the dynamics of ornithocomplexes on the territory of wind power plants (WPPs) is proposed. During the study, it was found that the parameters for the statistical analysis of ornithocomplexes on the territory of the wind farm are: the number of birds, the number of species, the time spent at the recording site and the activity coefficients of individuals of different species at different altitudes. The solution to the most frequently encountered problems in the analysis of bird migration in the wind farm zone is considered: 1) An algorithm has been developed for the primary statistical processing of information obtained by the route counting method and observations in accordance with the recommendations of the Scottish Natural Heritage Foundation. 2) The features of the application of correlation and regression analysis using the Student, Pearson and Fisher criteria are considered. 3) An algorithm for statistical analysis based on the trend approach is proposed. The considered statistical methods were tested on the results of migratory bird census on the territory of the Prymorsk-1 wind farm located on the coast of the Sea of Azov.
References
- Yeremieiev, V., Siokhin, V., Matsyura, A. & Bukreev, D. (2024). Statistical methods for processıng data on the impact of wind farms on birds. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 29, 293-304.
Details
| Primary Language | English |
|---|---|
| Subjects | Software Engineering (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | October 27, 2024 |
| Publication Date | September 30, 2024 |
| Submission Date | February 4, 2024 |
| Acceptance Date | March 6, 2024 |
| Published in Issue | Year 2024 |
