Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 6 4 2006 10.5194/nhess-6-629-2006 http://www.nat-hazards-earth-syst-sci.net/6/629/2006/ http://www.nat-hazards-earth-syst-sci.net/6/629/2006/nhess-6-629-2006.html http://www.nat-hazards-earth-syst-sci.net/6/629/2006/nhess-6-629-2006.pdf 629 635 2006-07-14 A neural network model for short term river flow prediction R. Teschl W. L. Randeu Graz University of Technology, Department of Broadband Communications, Graz, Austria This paper presents a model using rain gauge and weather radar data to predict the runoff of a small alpine catchment in Austria. The gapless spatial coverage of the radar is important to detect small convective shower cells, but managing such a huge amount of data is a demanding task for an artificial neural network. The method described here uses statistical analysis to reduce the amount of data and find an appropriate input vector. Based on this analysis, radar measurements (pixels) representing areas requiring approximately the same time to dewater are grouped. Adeloye, A. J. and De Munari, A.: Artificial neural network based generalized storage–yield–reliability models using the Levenberg-Marquardt algorithm, J. Hydrol., 326(1–4), 215–230, 2006. Aliev, R., Bonfig, K. W., and Aliew, F.: Soft Computing: Eine grundlegende Einführung, Verlag Technik, Berlin, 2000. Blöschl, G., Merz, R., and Piock-Ellena, U.: Flash-Flood Risk Assessment under the Impacts of Land Use Changes and River Engineering Works, Final Report, Institute of Hydraulics, Hydrol. Water Resour. Manage., Vienna University of Technology, 2001. Coulibaly, P. and Baldwin, C. K.: Nonstationary hydrological time series forecasting using nonlinear dynamic methods, J. Hydrol., 307, 164–174, 2005. Demuth, H. and Beale, M.: MATLAB Neural Networks Toolbox – User's Guide, Fifth Printing – Version 3, 1998. Hsu, K., Vijai Gupta, H., and Sorooshian, S.: Artificial neural network modeling of the rainfall-runoff process, Water Resour. Res., 31(10), 2517–2530, 1995. Imrie, C. E., Durucan, S., and Korre, A.: River flow prediction using artificial neural networks: generalisation beyond the calibration range, J. Hydrol., 233, 138–153, 2000. Kim, G. and Barros, A. P.: Quantitative flood forecasting using multisensor data and neural networks, J. Hydrol., 246, 45–62, 2001. Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models I: A discussion of principles, J. Hydrol., 10, 282–290, 1970. Pan, T.-Y. and Wang, R.-Y.: State space neural networks for short term rainfall-runoff forecasting, J. Hydrol., 297, 34–50, 2004. Solomatine, D. P. and Dulal, K. N.: Model trees as an alternative to neural networks in rainfall–runoff modelling, Hydrological Sciences – Journal des Sciences Hydrologiques, 48, 399–411, 2003. Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S.: A data driven algorithm for constructing ANN based rainfall-runoff models, Hydrol. Processes, 16, 1325–1330, 2002. Smith, P. L. (Chairman), Atlas, D., Bluestein, H. B., Chandrasekar, N. V., et al.: Weather Radar Technology Beyond NEXRAD, Committee on Weather Radar Technology Beyond NEXRAD, National Research Council, ISBN: 0-309-08466-0, 96 pages, 2002. Teschl, R. and Randeu, W. L.: An Artificial Neural Network-based Rainfall-Runoff Model using gridded Radar Data. Third European Conference on Radar in Meteorology and Hydrology (ERAD), Visby, Island of Gotland, Sweden, Paper nr.: ERAD3-A-00163, 2004. Toth, E., Brath, A., and Montanari, A.: Comparison of short-term rainfall prediction models for real-time flood forecasting, J. Hydrol., 239, 132–147, 2000.