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The use of artificial neural networks (ANNs) in modelling the hydrological processes has become a common approach in the last two decades, among side the traditional methods. In regard to the rainfall-runoff modelling, in both traditional and ANN models the use of ground rainfall measurements is prevalent, which can be challenging in areas with low rain gauging station density, especially in catchments where strong focused rainfall can generate flash-floods. The weather radar technology can prove to be a solution for such areas by providing rain estimates with good time and space resolution. This paper presents a comparison between different ANN setups using as input both ground and radar observations for modelling the rainfall-runoff process for Bahluet catchment, with focus on a flash-flood observed in the catchment.

[1] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000 I) Artificial Neural Networks in hydrology: I: preliminary concepts. Journal of Hydrology Engineering. 5(2), 115-123. DOI: 10.1061/(ASCE)1084-0699(2000)5:2(115)

[2] ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000 II) Artificial Neural Networks in hydrology: II: hydrological applications. Journal of Hydrology Engineering. 5(2), 124-137. DOI: 10.1061/(ASCE)1084-0699(2000)5:2(124)

[3] Hsu, K., Vijai Gupta, H., & Sorooshian, S. (1995), Artificial neural network modeling of the rainfall-runoff process. Water Resources Research. 31(10), 2517-2530. DOI: 10.1029/95WR01955

[4] Minns, A. W. & Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 41(3), 399-417.

[5] Dawson, C. W. & Wilby, R. (1998), An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal. 43(1), 47-66. DOI: 10.1080/02626669809492102

[6] Abrahart, R. J. & See, L. M. (2007), Neural network modelling of non-linear hydrological relationships. Hydrology and Earth System Sciences. 11, 1563-1579. DOI: 10.5194/hess-11-1563-2007

[7] Solomatine, D. P. & Ostfeld, A. (2008), Data-driven modelling: Some past experiences and new approaches. Journal of Hydroinformatics. 10(1), 3-22. DOI: 10.2166/hydro.2008.015

[8] Haykin, S. (1998), Neural Networks - A comprehensive foundation. Upper Saddle River, New Jersey, USA: Prentice-Hall.

[9] Hagan, M. T., Demuth, H. B., Beale, M. H. & De Jesus, O. (2014). Neural Network Design (2nd ed.). From http://hagan.okstate.edu/NNDesign.pdf.

[10]Levenberg, K. (1944). A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied Mathematics. 2: 164-168.

[11] Marquardt, D. (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters. SIAM Journal on Applied Mathematics. 11 (2): 431-441. DOI:10.1137/0111030

[12]WMO (2008), Guide to Hydrological Practices Volume I: Hydrology - From Measurement to Hydrological Information (6th ed.), World Meteorological Organization, No. 168

[13] Nash, J. E. & Sutcliffe, J. V. (1970), River flow forecasting through conceptual models I: A discussion of principles Journal of Hydrology. 10, 282-290. DOI: 10.1016/0022-1694(70)90255-6

[14]Coulibaly, P. & Baldwin, C. K. (2005), Nonstationary hydrological time series forecasting using nonlinear dynamic methods. J. Hydrol. 307, 164-174.

[15]Solomatine, D. P. & Dulal, K. N. (2003), Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal. 48, 399-411. DOI: 10.1623/hysj.48.3.399.45291

Mathematical Modelling in Civil Engineering

The Journal of Technical University of Civil Engineering of Bucharest

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