Abstract
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.
Highlights
Forecasting of river floods and prediction of areas to be flooded is an actual problem in territories located on banks of big rivers with regular or irregular flood behaviour
The main river monitoring data used for the flood estimation are river flow or river discharge, i.e., the volume of water flowing through the current cross-section of the river in a defined time interval
As a majority of the explanatory variables are chosen as the measurements taken in the near past, a symbolic regression can be applied to the flow forecasting in the near future
Summary
Forecasting of river floods and prediction of areas to be flooded is an actual problem in territories located on banks of big rivers with regular or irregular flood behaviour. In research [1], the modelling for the evaluation of aftermath of spring floods of the Daugava River is discussed with the estimation of the flooded areas. For this estimation a heightmap model of the investigated area is applied together with data from local river monitoring station. The application of linear and symbolic regression methods is discussed in Sections V and VI, correspondingly.
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