In the work, based on the previously constructed multifactor dynamic regression model of water level in the Iya River (Eastern Siberia), the authors forecast this indicator for June 2023 in three options: pessimistic, optimistic and neutral (base). A comparison of the forecasting results with the actual value of the water level confirmed the high adequacy of the model and good prospects for its future successful use to solve a wide range of applied and practical problems.

Keywords: regression model, river water level, lag time, seasonal variable, forecast, adequacy, criteria

The paper presents a refined regression model of water level dynamics in the Siberian river Iya, which includes six natural factors on the right side (the number of days with precipitation in the Sayan Mountains, average day and night temperatures for the month, the amount of precipitation, snow depth, average atmospheric pressure for the month ) taking into account the delay, as well as a specially generated seasonal variable. The high adequacy of the model is indicated by the values ​​of the criteria of multiple determination, Fisher, and the average relative error of approximation. The constructed model can be effectively used to solve a wide range of forecasting problems.

Keywords: regression model, river water level, lag time, seasonal variable, forecast

Analyses for the current publishes show that the problem of forecast water overflowing is actual and often causing a lot health threaten and other dangerouses. This article offers computing, analysis and development the regression model of the level of Ia river. The final model correspont the real data with proper level. The final calculation means that this model could be used for real forecast for defend the people from water's overflow.

Keywords: model, simulation, river, water level, flood, emergency, forecast, statistics, monitoring, analysis, iya river, Irkutsk region