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The study of the dynamics of the level and volume of water in the Aral Sea is an urgent scientific task due to the need to understand the mechanisms of natural and anthropogenic processes that have induced a radical change in its water and salt balance over the past 60 years. In particular, the study of the dynamics of water balance components in the basin of the Small Aral Sea is the most important task when planning scenarios for water use in the region. In the proposed work, based on the methods of machine learning (for the implementation of computational functions in the program), two statistical models were developed: a forecast model for the monthly values of river flow in the Syrdarya river and the forecast of variability of the water volume of the Small Aral Sea. Based on the simulation results, forecasts were made for the values of the Syrdarya drainage and the water volume of the Small Aral Sea. In conditions of low availability of field observations data, the operational estimates of the water balance component are the most important source of information on the changes occurring in the basin under investigation. The proposed technique can also be used to obtain initial conditions in experiments on hydrodynamic modeling, as well as to calculate climatic scenarios for the development of the hydrological system of the Aral Sea.

FORECASTING OF THE STATE OF THE SMALL ARAL SEA BASED ON OPEN DATA SOURCES