Application of Neural Network Modeling in Problems of Predicting the Level of River Floods

Abstract

The purpose of this article is to consider the theoretical and practical issues of developing neural network models for river flood forecasting (in case of the Belaya River near Ufa), as well as to implement the corresponding neural network in Python. To build a training sample, archival data from meteorological services and meteorological observation sites for the flood periods of the Belaya (Agidel) River in 2018–2022 were used. The following indicators were collected and analyzed: water level, water temperature, day and night air temperature, precipitation, snow depth, including information about the pre-flood condition of the snow cover. The software implementation of the neural network was performed using the PyTorch deep learning library; in addition, modules from the Matplotlib and Pandas libraries were used. The stability of the operation of this neural network was studied when the following parameters were changed: the optimizers used (Adam, Adamax and Rprop); learning rate coefficient; the number of neurons in the hidden layer; number of learning epochs. It is concluded that the developed neural network can be used to model the flood level when creating short-term forecasts. In order to move to longer-term forecasts in the future, it is planned to further expand the size of the factors in the training sample.