Regression-based Daugava River Flood Forecasting and Monitoring

Vitaly Bolshakov


The paper discusses the application of linear and symbolic regression to forecast and monitor river floods. Main tasks of the research are to find an analytical model of river flow and to forecast it. The challenges are a small set of flow measurements and a small number of input factors. Genetic programming is used in the task of symbolic regression. To train the model, historical data of the Daugava River monitoring station near Daugavpils city are used. Several regression scenarios are discussed and compared. Models obtained by the methods discussed in the research show good results and applicability in predicting the river flow and forecasting of the floods.


Floods; forecasting; genetic programming; monitoring; regression

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