Predictive performance of linear regression models in estimation of Artemia salina abundance using field and remote sensing data

Krivoguz D., Borovskaya R.

Azov-Black Sea Branch of “VNIRO” (“AzNIIRKH”),

RF, Rostov-on-Don, Beregovaya St., 21v


DOI: 10.33075/2220-5861-2021-2-88-95

UDC 544.623


   This research has been aimed at finding the possibilities for application of the linear regression models, as a part of the machine learning methods, in visual representation of the spatial patterns of Artemia salina distribution in the Southern Sivash. Development of such models allows for estimation of A. salina biomass in water bodies with high accuracy. For investigation of maximum absorption levels in different parts of the light spectrum, spectral signatures at all the monitoring stations have been compared with the satellite data, and the analysis of the absorption spectra for astaxanthin and hemoglobin has been conducted with a spectrophotometer.

   As a result, Sentinel-2 satellite looks very promising as a key spatial data provider that can be of major help in increasing the frequency of A. salina monitoring in the Southern Sivash. The linear regression models, fitted by the third and the fourth degree polynomials, have shown satisfactory results, suitable for their subsequent use in fisheries. On the other hand, it should be noted that these models are slightly prone to overfitting, which to some extent can distort further forecasts feeding upon the new data. In turn, linear regression models fitted by a polynomial of the first degree show less accurate results, but their advantages include the lack of tendency to overfit.

  It is also worth noting that small-sized datasets within the scope of this investigation do not appear to be problematic, and simple machine learning algorithms can provide good accuracy results, which are suitable for further application in this field.

Keywords: Remote sensing, spectral analysis, Artemia salina, Sivash, machine learning.

To quote: Krivoguz, D., and R. Borovskaya. “Predictive Performance of Linear Regression Models in Estimation of Artemia Salina Abundance Using Field and Remote Sensing Data.” Monitoring Systems of Environment no. 2 (June 24, 2021): 88–95. doi:10.33075/2220-5861-2021-2-88-95.

Full text in PDF


  1. Buongiorno Nardelli B. et al. A re-analysis of Black Sea surface temperature // J. Mar. Syst. 2010. Vol. 79, № 1–2. P. 50–64.
  2. Krivoguz D., Bespalova L. Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS // Russ. J. Earth Sci. 2020. Vol. 20, № 1. P. 1–12.
  3. Krivoguz D. Methodology of physiography zoning using machine learning: A case study of the Black Sea // Russ. J. Earth Sci. 2020. Vol. 20, № 1. P. 1–10.
  4. Demchev D. et al. Sea Ice Drift Tracking From Sequential SAR Images Using Accelerated-KAZE Features // IEEE Trans. Geosci. Remote Sens. 2017. Vol. 55, № 9. P. 5174–5184.
  5. McFeeters S.K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features // Int. J. Remote Sens. 1996. Vol. 17, № 7. P. 1425–1432.
  6. Hung Trinh L., Tuyen Vu D. Application of remote sensing technique for drought assessment based on normalized difference drought index, a case study of Bac Binh district, Binh Thuan province (Vietnam) // Russ. J. Earth Sci. 2019. Vol. 19. P. ES2003.
  7. Sovga E.Е., Eryemina E.S., Khmara T. V. Water Balance in the Sivash Bay as a Result of Variability of the Natural-Climatic and Anthropogenic Factors // Phys. Oceanogr. 2018. Vol. 25, № 1. P. 67–76.
  8. Vesnina L.V., Permyakova G.V. Dynamics of number and distribution of uneven-age individuals of Artemia in deep-water Bolshoe Yarove lake (Altaysky Kray) // Tomsk State Univ. J. Biol. 2013. Vol. 1. P. 89–102.
  9. Shadrin N. V., Anufriieva E. V., Shadrina S.N. Brief review of phototrophs in the Crimean hypersaline lakes and lagoons: diversity, ecological role, the possibility of using // Mar. Biol. J. 2017. Vol. 2, № 2. P. 80–85.
  10. Semik A.M., Saenko E.M., Zamyatina E.A. Current status of the Brine srimp population Artemia Leach, 1819 in the Eastern Sivash Bay // Aquat. Bioresour. Environ. Aquatic Bioresources & Environment, FSBSI VNIRO, Azov-Black Sea Branch of the FSBSI VNIRO (AzNIIRKH), 2019. Vol. 2, № 2. P. 45–56.
  11. Gajardo G.M., Beardmore J.A. The Brine Shrimp Artemia: Adapted to Critical Life Conditions // Front. Physiol. Frontiers, 2012. Vol. 3. P. 185.
  12. Shaala N.M.A. et al. Lethal Concentration 50 (LC50) and Effects of Diuron on Morphology of Brine Shrimp Artemia Salina (Branchiopoda: Anostraca) Nauplii // Procedia Environ. Sci. Elsevier BV, 2015. Vol. 30. P. 279–284.
  13. Litvinenko L.I. et al. Methodical recomendations for stock assessment and prediction of recomendational amount of catching Artemia. Moskow, 2019. 50 p.
  14. Anufriieva E., Shadrin N. The long‐term changes in plankton composition: Is Bay Sivash transforming back into one of the world’s largest habitats of Artemia sp. (Crustacea, Anostraca)? // Aquac. Res. Blackwell Publishing Ltd, 2020. Vol. 51, № 1. P. 341–350.
  15. Gilchrist B.M., Green J. The pigment of Artemia. // Proc. R. Soc. Lond. B. Biol. Sci. The Royal SocietyLondon, 1960. Vol. 152. P. 118–136.
  16. Boonyaratpalin M. et al. Effects of β-carotene source, Dunaliella salina, and astaxanthin on pigmentation, growth, survival and health of Penaeus monodon // Aquac. Res. Blackwell Publishing Ltd., 2001. Vol. 32, № SUPPL. 1. P. 182–190.
  17. Czygan F.C. On the Metabolism of Carotenoids in the Crustacean Artemia salina // Zeitschrift fur Naturforsch. – Sect. B J. Chem. Sci. Verlag der Zeitschrift für Naturforschung, 1968. Vol. 23, № 10. P. 1367–1368.
  18. Amarouayache M., Kara M.H. Aspects of life history of Artemia salina (Crustacea, Branchiopoda) from Algeria reared in different conditions of salinity // Vie milieu – Life Environ. 2017. Vol. 67, № 1. P. 15–20.
  19. Shadrin N., Yakovenko V., Anufriieva E. Suppression of Artemia spp. (Crustacea, Anostraca) populations by predators in the Crimean hypersaline lakes: A review of the evidence // Int. Rev. Hydrobiol. Wiley-VCH Verlag, 2019. Vol. 104, № 1–2. P. 5–13.
  20. Ha N.-T. et al. Detecting Multi-Decadal Changes in Seagrass Cover in Tauranga Harbour, New Zealand, Using Landsat Imagery and Boosting Ensemble Classification Techniques // ISPRS Int. J. Geo-Information. Multidisciplinary Digital Publishing Institute, 2021. Vol. 10, № 6. P. 371.
  21. Vandenberg C.J., Matthews C.M., Trotman C.N.A. Variant Subunit Specificity in the Quaternary Structure of Artemia Hemoglobin // Mol. Biol. Evol. Society for Molecular Biology and Evolution, 2002. Vol. 19, № 8. P. 1288–1291.
  22. Clegg J.S., Trotman C.N.A. Physiological and Biochemical Aspects of Artemia Ecology // Artemia: Basic and Applied Biology. Springer Netherlands, 2002. P. 129–170.
  23. Uyanık G.K., Güler N. A Study on Multiple Linear Regression Analysis // Procedia – Soc. Behav. Sci. 2013. Vol. 106. P. 234–240.
  24. Seber G.A., Lee A.J. Linear regression analysis. Wiley-Interscience, 2003. 557 p.
  25. Weisberg S. Applied linear regression. Wiley-Interscience, 2005. 310 p.
  26. Pesaran M.H., Smith R.J. A Generalized R^2 Criterion for Regression Models Estimated by the Instrumental Variables Method // Econometrica. 1994. Vol. 62, № 3. P. 705.