Base Flow Index Estimation on Gauged and Ungauged Catchments in Hungary Using Digital Filter, Multiple Linear Regression and Artificial Neural Networks

  • Zsolt Jolánkai Budapest University of Technology and Economics
  • László Koncsos Budapest University of Technology and Economics

Abstract

A country scale analysis of diffuse source nutrient emissions have been undertaken previously on small catchments level using the MONERIS model, which needed a proper estimation of surface and subsurface runoff differentiation to support or contradict its own water budget based method. As reliable, country scale base flow estimation has not been available for the country at the time of the study, this knowledge gap has been tried to be filled by the current work. This has been done using multiple methods. Digital filter have been applied on continuous river discharge data on gauged catchments in order to determine base flow indices (BFI). Using multiple linear regression (MLR) and artificial neural networks (ANN), climatic, soil and land use properties of the catchments have been used to extend base flow indices to ungauged catchments. MLR brought acceptable results (adjusted r2 values around 0.7), however it proved to be sensitive of the selection of catchments used for validation, and therefore a mean of prediction by thirty different regression equation was used for the estimation. ANN was less sensitive for the change of the variables included and the number of nodes used for the learning. The results are comparable with the MLR method and show good agreement in most of the areas, however in some part of the country the two approach show significant differences in the predicted BFI values.

Keywords: moneris, base flow index, multiple linear regression, artificial neural network, base flow separation, digital filter
Published online
2017-11-30
How to Cite
Jolánkai, Z., & Koncsos, L. (2018). Base Flow Index Estimation on Gauged and Ungauged Catchments in Hungary Using Digital Filter, Multiple Linear Regression and Artificial Neural Networks. Periodica Polytechnica Civil Engineering, 62(2), 363-372. https://doi.org/10.3311/PPci.10518
Section
Research Article