Landslides are one of the most important problems on Kerch Peninsula. Rapidly increasing anthropogenic pressure, which has been particularly could be seen in recent years, leads to destabilization of slopes and significant economic and social damage. Nowdays, economic activities for the further development of Kerch Peninsula, urbanization and the construction of new infrastructure facilities may lead to destabilization of slopes and activation of landslides. Application of an integrated mathematical model, helped us to reflect the current state of Kerch Peninsula in the context of solving this problem. With the help of field research, we mapped a complex of landslide areas, which made it possible to identify the main types of them occurring here-earth and earthwork slips landslides, and in the coastal zone-landfalls and caving slides, total area of which reaches 87.5 km 2 (7.4% of the research area territory). We chose an optimal method under the given conditions-the weight of evidence. The approach presented in this paper allows us to classify the territory according to the degree of its susceptibility to landslides with rather high accuracy and analyze the existing situation in this area and analyze possible scenarios for its development. We zoned the territory of the Kerch peninsula into 3 classes-stable, unstable and unsusceptible according to the degree of landslide probable occurrences. The susceptibility analysis revealed that the factors causing the activation of landslides on the Kerch Peninsula are the slope steepness from 20 to 40 degrees, the anthropogenic impact, excessive salinity of the soil cover and the lithological composition of the terrain.

Problem of area's zoning is very important and is one of the main problems of modern geographical science. Our point is to from a modern approach, based on the machine learning methods to provide zoning of any area. Key ideas of this methodology, that any distribution of factors that form any geographical system grouped around some clusters-unique zones that represents specific nature conditions. Formed methodology based on several stages-selection of data and objects for analysis, data normalization, assessment of predisposition of data for clustering, choosing the optimal number of clusters, clustering and validation of results. As an example, we tried to zone a surface layer of the Black Sea. We find that optimal number of unique zones is 3. Also, we find that the key driver of zone forming is a location of the rivers. Thus, we can say, that applying a machine learning approach in area's zoning tasks helps us increasing the quality of nature using and decision-making processes.

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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.

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.