Application of Artificial Neural Networks for estimating index floods

Viliam ŠIMOR; Kamila HLAVČOVÁ; Silvia KOHNOVÁ; Ján SZOLGAY

doi:10.2478/v10126-012-0014-7

Viliam ŠIMOR Faculty of Civil Engineering, Slovak University of Technology
Kamila HLAVČOVÁ Faculty of Civil Engineering, Slovak University of Technology
Silvia KOHNOVÁ Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovak Republic
Ján SZOLGAY Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovak Republic

DOI: https://doi.org/10.2478/v10126-012-0014-7

Keywords: index flood, catchment predictors, Artificial Neural Networks (ANNs), multiple regression models

Abstract

This article presents an application of Artificial Neural Networks (ANNs) and
multiple regression models for estimating mean annual maximum discharge (index flood)
at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas
ranging from 20 to 300 km2. Using the objective clustering method, the catchments were
divided into ten homogeneous pooling groups; for each pooling group, mutually independent
predictors (catchment characteristics) were selected for both models. The neural
network was applied as a simple multilayer perceptron with one hidden layer and with
a back propagation learning algorithm. Hyperbolic tangents were used as an activation
function in the hidden layer. Estimating index floods by the multiple regression models
were based on deriving relationships between the index floods and catchment predictors.
The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation
coefficients. The results showed the comparative applicability of both models with slightly
better results for the index floods achieved using the ANNs methodology.

Author Biography

Viliam ŠIMOR, Faculty of Civil Engineering, Slovak University of Technology

Radlinského 11, 813 68 Bratislava, Slovak Republic