Methodological aspects of natural territories zoning using machine learning

Krivoguz, Denis; Borovskaya, R.V.

doi:10.33075/2220-5861-2020-1-14-20

Cited by 4 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its maximum depth reaches the mark of 2258 meters (Figure 1). The total area of the Black Sea is 420325 km 2 , and with the Sea of Azov -462000 km 2 . The average seasonal cycle of geostrophic circulation in the Black Sea (according to Ivanov and Belokopytov [8]):  from January to March -a single cyclonic rotation cantered in the eastern part of the sea, the western circulation is weakly expressed;  from April to May -a single cyclonic rotation with a centre in the western part of the sea, the eastern cycle is weakly expressed;  from June to July -two cycles, the western more intense;  from August to September -two cycles, the eastern one is more intense;  from October to December -two cycles of equal intensity.…”

Section: Research Areamentioning

confidence: 99%

“…In general, it is the least studied of all the variables that characterize the oceans or climate, mainly due to a lack of historical data. This is primarily due to the fact that until recently, our knowledge of the distribution of salinity in the oceans was based on inconsistent measurements [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…For marine organisms, any significant changes in the physio-chemical regime of the waterbody are not favourable [2]. Many marine and freshwater organisms living in Black Sea basins show a significant decrease in their size.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial analysis of salinity distribution patterns in upper layers of the Black Sea

Krivoguz

Mal’ko²,

Semenova³

2020

E3S Web Conf.

View full text Add to dashboard Cite

Salinity is one of the most important factors that primarily determines the level of seawater’s density and, consequently, the movement of water masses in the World Ocean. Spatial distribution of the salinity in different layers of the Black Sea are associated with varying levels of water balance seasonal variability and, general circulation of Black Seas waters and in the surface layer has a seasonal structure. To study spatial distribution of salinity in upper layers of the Black Sea we’ve used data from Copernicus Marine Environment Monitoring Service, that were processed and aggregate by seasons and depth. We found that the most fluctuated layer is a top layer (up to 2.8 m) and the highest values Black Sea salinity reaches near the Bosporus Strait, where more saline water from the Sea of Marmara connected with fresher water of the Black Sea. Also we found that the impact of the river flows, mixing of the water, water regime of the sea decreasing with depth, so in the bottom of the upper layer the spatial fluctuation of the salinity is minimal and reaches about ±3‰, while in the depth of 2.8 m its reaches ±12-15‰.The lowest level of salinity through all of the upper layer (0-50 m) lays around the seashore and north-western part of the sea.

show abstract

Section: Research Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial analysis of salinity distribution patterns in upper layers of the Black Sea

Krivoguz

Mal’ko²,

Semenova³

2020

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…Salinity belongs to the group of environmental factors that significantly affect the formation of a particular type of coastal marine ecosystem [2]. A number of studies shows that in some cases, triggered by anthropogenic impact, such as a decrease in river flow as a result of watercourses regulation, as well as natural causes, salinity can be a limitation factor for marine organisms.…”

Section: Introductionmentioning

confidence: 99%

Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Borovskaya

Krivoguz

Chernyi

et al. 2022

JMSE

View full text Add to dashboard Cite

Knowledge of the spatio-temporal distribution of salinity provides valuable information for understanding different processes between biota and environment, especially in hypersaline lakes. Remote sensing techniques have been used for monitoring different components of the environment. Currently, one of the biggest challenges is the spatio-temporal monitoring of the salinity level in water bodies. Due to some limitations, such as the inability to be located there permanently, it is difficult to obtain these data directly. In this study, machine learning techniques were used to evaluate the salinity level in hypersaline East Sivash Bay. In total, 93 in situ data samples and 6 Sentinel-2 datasets were used, according to field measurements. Using linear regression, random forest and AdaBoost models, eight water salinity evaluation models were built (six with simple, one with random forest and one with AdaBoost). The accuracy of the best-fitted simple linear regression model was 0.8797; for random forest, it was equal, at 0.808, and for AdaBoost, it was −0.72. Furthermore, it was found that with an increase in salinity, the absorbing light shifts from the ultraviolet part of the spectrum to the infrared and short-wave infrared parts, which makes it possible to produce continuous monitoring of hypersaline water bodies using remote sensing data.

show abstract

“…Ecosystem analysis -is one of the most important scientific methods of receiving information about characteristics of any area [Bourlière and Hadley, 1973]. Main goal of the ecosystem analysis is studying and assignment of natural ecological systems, defining their functional potential, georeferencing, ecological and social position [Krivoguz and Borovskaya, 2020;Milns et al, 2010]. Also, it can give an informational basis for more objective evaluation of environmental and resource state for future optimization, predicting of their state, qualitative and quantitative environmental monitoring, etc.…”

Section: Introductionmentioning

confidence: 99%

New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks

Krivoguz¹,

Chernyi²,

Manukov³

2021

Russ. J. Earth. Sci.

View full text Add to dashboard Cite

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.

show abstract

Methodological aspects of natural territories zoning using machine learning

Cited by 4 publications

References 0 publications

Spatial analysis of salinity distribution patterns in upper layers of the Black Sea

Spatial analysis of salinity distribution patterns in upper layers of the Black Sea

Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

New Approach for Analyzing Marine Ecosystem Structure Using Bayesian Networks

Contact Info

Product

Resources

About