Articles | Volume 9, issue 4
Hydrol. Earth Syst. Sci., 9, 313–321, 2005
https://doi.org/10.5194/hess-9-313-2005

Special issue: Advances in flood forecasting

Hydrol. Earth Syst. Sci., 9, 313–321, 2005
https://doi.org/10.5194/hess-9-313-2005

  07 Oct 2005

07 Oct 2005

Simulation of flood flow in a river system using artificial neural networks

R. R. Shrestha1, S. Theobald2, and F. Nestmann2 R. R. Shrestha et al.
  • 1Department of Hydrological Modelling, UFZ-Centre for Environmental Research Leipzig-Halle, Brückstrasse 3a, 39114 Magdeburg, Germany
  • 2Institute for Water Resources Managment, Hydraulic and Rural Engineering, University of Karlsruhe, D-76128 Karlsruhe, Germany
  • Email for corresponding author: rajesh.shrestha@ufz.de

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.