Method of Data Preprocessing on the Basis of Pulse Neural Network to Improve the Accuracy of Water Level Forecast on the Example of Ufa City of the Republic of Bashkortostan

Abstract

The developed method of data preprocessing based on impulse neural network is considered. The essence of this method is to improve the quality of the initial dataset by minimising data noise. The peculiarity is to improve the accuracy of the predicted values of the time series of water levels obtained at the output of the pulse neural network. Retrospective data were obtained from hydrological posts (river gauge) and automatic stations with the help of FSUE «Centre of Register and Cadastre» from 01.01.1997 to 30.06.2023. On the example of hydrological post 76289 (Ufa) the experiment on forecasting of water levels with the help of the developed system «Flood 2.0» was carried out. The experiment proves the efficiency of the data preprocessing method developed in this study to improve the accuracy of water level forecasts.