The use of recurrent neural networks to predict the water level in the Amur River are consider. The advantages of using such networks in comparison with traditional machine learning methods are described. Various architectures of recurrent networks are compared, and hyperparameters of the model are optimized. The developed model based on long-term short-term memory (LSTM) has demonstrated high prediction accuracy, surpassing traditional methods. The results obtained can be used to improve the effectiveness of monitoring water resources and flood prevention.

Keywords: time series analysis, Amur, water level, forecasting, neural networks, recurrent network