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.

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