BISAC SCI019000 Earth Sciences / General
Aquatic ecosystems of the Black Sea are complex multiparametric systems with a hierarchical structure. Thus, the main goal of our research was to investigate possibilities of using Bayesian networks to study the structure fo the natural systems in the Black Sea. We used CMEMS Black Sea environmental dataset, which consists of 7 different variables, that, in our opinion, can describe structural relations in the Black Sea ecosystem – sea surface temperature and salinity, concentrations of nitrates and phosphates, amount of chlorophyll-a and net primary production and also dissolved oxygen concentration. We think, that these variables can generally define interactions in water environment of the Black Sea, organisms, that live there and human activity. As a modelling result, we receive a structure of environmental variables interactions. At the top of this structure is a dissolved oxygen, as a final result of the ecosystem functioning. Further, we think it’s more appropriate to use Dynamic Bayesian networks for investigation of spatio-temporal changes to distinguish main drivers of changes and provide more balanced management of natural territories.
machine learning, Black Sea, Bayesian networks, ecosystem analysis
1. Barratt, L., Black sea oceanography, Reviews in Fish Biology and Fisheries, 3(2), 199-200, doihttps://doi.org/10.1007/BF00045240,1993.
2. Bendtsen, M., Gated Bayesian Networks, 212 pp., Linköping University, Linköping, 2017.
3. Bourlière, F., and M. Hadley, Ecosystem Analysis: Combination of Qualitative and Quantitative Approaches, in Ecological studies, pp. 1-6, Springer, Berlin, Heidelberg,doihttps://doi.org/10.1007/9783642855870_1, 1973.
4. Chen, S. H., and C. A. Pollino, Good practice in Bayesian network modelling, Environmental Modelling and Software, 37, 134-145, doihttps://doi.org/10.1016/j.envsoft.2012.03.012,2012.
5. Cooper, G. F., and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, 9(4), 309-347,doihttps://doi.org/10.1007/bf00994110, 1992.
6. Friedman, N., D. Geiger, and M. Goldszmidt, Bayesian Network Classifiers, Machine Learning, 29(23), 131-163, doihttps://doi.org/10.1023/a:1007465528199, 1997.
7. Havron, A., C. Goldfinger, S. Henkel, B. G. Marcot,C. Romsos, and L. Gilbane, Mapping marine habitat suitability and uncertainty of Bayesian networks: a case study using Pacific benthic macrofauna, Ecosphere, 8(7),e01,859, doihttps://doi.org/10.1002/ecs2.1859, 2017.
8. Ivanov, V., and V. Belokopytov, Oceanography of the Black Sea, 212 pp., ECOSY Gidrofizika, Sevastopol, 2011.
9. Koski, T. J., and J. Noble, A Review of Bayesian Networks and Structure Learning, Mathematica Applicanda, 40(1),doihttps://doi.org/10.14708/ma.v40i1.278, 2012.
10. Krivoguz, D., Methodology of physiography zoning using machine learning: A case study of the Black Sea, Russian Journal of Earth Sciences, 20(1), 1-10,doihttps://doi.org/10.2205/2020ES000707, 2020.
11. Krivoguz, D., and R. Borovskaya, Methodological aspects of natural territories zoning using machine learning, Monitoring systems of environment, 39(1), 13-20,doihttps://doi.org/10.33075/22205861202011420, 2020.
12. Landuyt, D., S. Broekx, R. D’hondt, G. Engelen, J. Aertsens, and P. L. Goethals, A review of Bayesian belief networks in ecosystem service modelling, Environmental Modelling & Software, 46, 1-11,doihttps://doi.org/10.1016/j.envsoft.2013.03.011, 2013.
13. Lehikoinen, A., J. Olsson, L. Bergström, U. Bergström, A. Bryhn, R. Fredriksson, and L. Uusitalo, Evaluating complex relationships between ecological indicators and environmental factors in the Baltic Sea: A machine learning approach, Ecological Indicators, 101,117-125, doihttps://doi.org/10.1016/j.ecolind.2018.12.053, 2019.
14. Lima, L., S. Masina, S. A. Ciliberti, E. L. Peneva, S. Cretí, L. Stefanizzi, R. Lecci, F. Palermo, G. Coppini, N. Pinardi, and A. Palazov, Black Sea Physical Reanalysis (CMEMS BSCurrents),doihttps://doi.org/10.25423/cmcc/blksea_reanalysis_phys_007_004,2020.
15. Marcot, B. G., Metrics for evaluating performance and uncertainty of Bayesian network models, Ecological Modelling, 230, 50-62,doihttps://doi.org/10.1016/j.ecolmodel.2012.01.013, 2012.
16. Marcot, B. G., and T. D. Penman, Advances in Bayesian network modelling: Integration of modelling technologies, Environmental Modelling & Software, 111(1), 386-393, doihttps://doi.org/10.1016/j.envsoft.2018.09.016, 2019.
17. Margaritis, D., Learning Bayesian network model structure from data, Tech. rep., Carnegie Mellon Univ Pittsburgh Pa School of Computer Science, 2003.
18. Milns, I., C. M. Beale, and V. A. Smith, Revealing ecological networks using Bayesian network inference algorithms, Ecology, 91(7), 1892-1899, 2010.
19. Nagarajan, R., M. Scutari, and S. Lèbre, Bayesian Networks in R: with Applications in Systems Biology, 1-157 pp., Springer New York, doihttps://doi.org/10.1007/9781461464464,2013.
20. Ozsoy, E., and U. Unluata, Oceanography of the Black Sea: a review of some recent results, Earth Science Reviews,42, 231-272, 1997.
21. Scutari, M., Learning Bayesian networks with the bnlearnR Package, Journal of Statistical Software, 35(3), 1-22,doihttps://doi.org/10.18637/jss.v035.i03, 2010.
22. Singh, M., and M. Valtorta, Construction of Bayesian network structures from data: A brief survey and an efficient algorithm, International Journal of Approximate Reasoning, 12(2), 111-131, doihttps://doi.org/10.1016/0888613X(94)00016V, 1995.
23. Tamaychuk, A. N., The space heterogeneity of natural conditions and the division of the Black Sea, Regional Research of Russia, 149(2), 30-50, 2017.
24. Trifonova, N., A. Kenny, D. Maxwell, D. Duplisea,J. Fernandes, and A. Tucker, Spatiotemporal Bayesian network models with latent variables for revealing trophic dynamics and functional networks in fisheries ecology, Ecological Informatics, 30, 142-158,doihttps://doi.org/10.1016/j.ecoinf.2015.10.003, 2015.
25. Trifonova, N., D. Maxwell, J. Pinnegar, A. Kenny, and A. Tucker, Predicting ecosystem responses to changes in fisheries catch, temperature, and primary productivity with a dynamic Bayesian network model, ICES Journal of Marine Science, 74(5), 1334-1343,doihttps://doi.org/10.1093/icesjms/fsw231, 2017.
26. Zhou, J., P. G. Asteris, D. J. Armaghani, and B. T. Pham, Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models, Soil Dynamics and Earthquake Engineering, 139(1), 106,390,doihttps://doi.org/10.1016/j.soildyn.2020.106390, 2020.
