Using Neural Network Methods for Peak Water Level Prediction: A Case Study for the Dvina-Pechora Basin Rivers

The paper examines the implementation of neural network methods for predicting peak water levels during the period of spring ice drift by the example of the Sukhona, Northern Dvina, and Pechora rivers. All considered neural network methods have shown high efficiency according to the criteria recommended by the Hydrometcenter of Russia and surpassed regression dependencies in the skill of forecasts. When using the method of training artificial neural networks, the standard error of prediction is reduced by approximately 10-20% as compared with regression dependencies.