The development of a hydrological model of the Bednja River based on a recurrent neural network Hydrology is a science that studies distribution and movement of water below and above the Earth's surface and in its atmosphere. The information obtained from the analysis of the behaviour and movement of water through the hydrological cycle is of great importance in areas such as water resources management, risk management of water-related hazards, infrastructure design, electricity generation and environmental protection. Hydrological modelling is a powerful tool that enables simulation and understanding of interactions between different components of the hydrological cycle, and the choice of a model type depends on the research objectives, available data, computer resources and characteristics of the hydrological system that is being modelled. Artificial intelligence has been increasingly integrated into hydrological modelling, in order to overcome the limitations of classic models and enhance their accuracy and predictive capabilities. One of the subfields of artificial intelligence are artificial neural networks, which are used to determine nonlinear relationships and time dependencies in hydrological data. In this paper, the Python programming language was used for the development and implementation of a hydrological model based on a recurrent neural network. This specific neural network architecture was chosen for its ability to capture patterns and temporal dependencies in data sets. A model of the Bednja basin was created, which has a flood regime dependent on the amount of precipitation. The model predicts flows at the Ludbreg hydrological station, based on previous data on precipitation and flows from upstream stations.