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Nonlin. Processes Geophys., 13, 443-448, 2006
https://doi.org/10.5194/npg-13-443-2006
© Author(s) 2006. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
 
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 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.

Citation: Piotrowski, A., Napiórkowski, J. J., and Rowiński, P. M.: Flash-flood forecasting by means of neural networks and nearest neighbour approach – a comparative study, Nonlin. Processes Geophys., 13, 443-448, https://doi.org/10.5194/npg-13-443-2006, 2006.
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