RUSSIAN JOURNAL OF EARTH SCIENCES, VOL. 20, ES2003, doi:10.2205/2020ES000707, 2020

Table 1. Estimated Data Accuracy Results for Temperature and Salinity. From Left Side in Each Row – for 1995–2015 Data. From Right – for 2005–2015
Feature BIAS v4 DMS v4
SST (° C) $-0.07/-0.07$ 0.58/0.59
T (° C) 0–100 m $-0.02/0.025$ 0.87/0.74
T (° C) 100–300 m $-0.03/-0.003$ 0.15/0.09
T (° C) 300–800 m $-0.02/-0.02$ 0.11/0.05
S (psu) 0–100 m $-0.014/0.002$ 0.33/0.26
S (psu) 100–300 m $-0.006/0.009$ 0.19/0.15
S (psu) 300–800 m $-0.005/-0.002$ 0.05/0.03

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Citation: Krivoguz Denis (2020), Methodology of physiography zoning using machine learning: A case study of the Black Sea, Russ. J. Earth Sci., 20, ES2003, doi:10.2205/2020ES000707.
 

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