Articles | Volume 13, issue 4
Nonlin. Processes Geophys., 13, 443–448, 2006
https://doi.org/10.5194/npg-13-443-2006

Special issue: Complex dynamics in geosciences: analysis techniques for observational...

Nonlin. Processes Geophys., 13, 443–448, 2006
https://doi.org/10.5194/npg-13-443-2006

  25 Aug 2006

25 Aug 2006

Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study

A. Piotrowski, J. J. Napiórkowski, and P.M. Rowiński A. Piotrowski et al.
  • Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland

Abstract. In this paper, Multi-Layer Perceptron and Radial-Basis Function Neural Networks, along with the Nearest Neighbour approach and linear regression are utilized for flash-flood forecasting in the mountainous Nysa Klodzka river catchment. It turned out that the Radial-Basis Function Neural Network is the best model for 3- and 6-h lead time prediction and the only reliable one for 9-h lead time forecasting for the largest flood used as a test case.