Oleg Makarynskyy

Ipswich City Council, Australia;

Emergency Management Section, Office of the General Manager, Environment and Sustainability Department

Scopus profile: link
Researcher ID: B-7907-2016
Google Scholar profile: link
ID ORCID: https://orcid.org/0000-0002-0505-5882

Selected Publications:

  1. Adnan, R. M., Sadeghifar, T., Alizamir, M., Azad, M. T., Makarynskyy, O., Kisi, O., Barati, R., Ahmed, K. O. (2023). Short-term probabilistic prediction of significant wave height using bayesian model averaging: Case study of chabahar port, Iran. Ocean Engineering, 272, 113887. https://doi.org/10.1016/j.oceaneng.2023.113887 
  2. Makarynskyy, O., Makarynska, D, Vale, E, Hatton, P. (2022). Flood-tide interactions within the lower Manning River during 2020 and 2021 flood events. 2022 Floodplain Management Australia National Conference. Available at: https://www.floods.asn.au/client_images/2350621.pdf
  3. Makarynskyy, O. (Ed.) (2021). Marine Hydrocarbon Spill Assessments. From Baseline Information through to Decision Support Tools. Elsevier. https://doi.org/10.1016/C2018-0-00344-8
  4. Greenslade, D., Hemer, M., Babanin, A., Makarynskyy, O., Zhong, A., Zieger, S. (2020). 15 priorities for wind-waves research: An Australian perspective. Bulletin of the American Meteorological Society, 101 (4), E446–E461.
  5. Ghorbani, M. A., Asadi, H., Makarynskyy, O., Makarynska, D., Yaseen, Z. M. (2017). Augmented chaos-multiple linear regression approach for prediction of wave parameters. Engineering Science and Technology, an International Journal, 20 (3), 1180–1191. http://doi.org/10.1016/j.jestch.2016.12.001
  6. Ghorbani, M. A., Khatibi, R., FazeliFard, M. H., Naghipour, L., Makarynskyy, O. (2015). Short-term wind speed predictions with machine learning techniques. Meteorology and Atmospheric Physics, 128 (1), 57–72. http://doi.org/10.1007/s00703-015-0398-9
  7. Makarynskyy, O., Makarynska, D., Rayson, M., Langtry, S. (2015). Combining deterministic modelling with artificial neural networks for suspended sediment estimates. Applied Soft Computing, 35, 247–256. http://doi.org/10.1016/j.asoc.2015.05.044