Dept. of Forestry, Faculty of Natural Resources, University of Guilan. O.Box 1144, Somehsara, Iran. 2- Dept. of Water resource Engineering, Lund University, Sweden
In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under investigation. However, they have been known as black-box models due to their problem in providing insight into the relationship learned. In this study, firstly we develop a rainfall-runoff model using an ANN approach, and secondly we describe different approaches including Neural Interpretation Diagram, Garson?s algorithm, and randomization approach to understand the relationship learned by the ANN model. The results indicate that ANNs are promising tools not only in accurate modelling of complex processes but also in providing insight from the learned relationship, which would assist the modeller in understanding of the process under investigation as well as in evaluation of the model.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000a) Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. ASCE, 5, 115-123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000b) Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Eng. ASCE, 5, 124-137.
Dawson, C.W. & Wilby, R.L. (1998) An artificial neural network approach to rainfall-runoff modeling. Hydrol. Sci. J. 43, 47-65.
Dawson, C.W. & Wilby, R.L. (2001) Hydrological modelling using artificial neural networks. Prog. Phys. Geog. 25, 80-108.
Garson, G.D. (1991) Interpreting neuralnetwork connection weights. Artificial Intell. Expert. 6, 47-51.
Giustolisi, O. & Laucelli, D. (2005) Improving generalization of artificial neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 50, 439-457.
Hsu, K.L., Gupta, H.V. & Sorooshian, S. (1995) Artificial neural network modeling of the rainfall-runoff process. Wat. Resour. Res. 31, 2517-2530.
Jain, A. & Srinivasulu, S. (2006) Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. J. Hydrol. 317, 291-306.
Kingston, G.B. Maier, H.R. & Lambert, M.F. (2003) Understanding the mechanisms modelled by artificial neural networks for hydrological prediction. MODSIM 2003 congress, Townsville, Australia, July 14-17, Vol. 2, 825-830.
Lorrai, M. & Sechi, G.M. (1995) Neural nets for modelling rainfall-runoff transformations. Wat. Res. Manage. 9, 299-313.
Maier, H.R. & Dandy, G.C. (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Modelling Software 15, 101- 124.
Minns, A.W. & Hall, M.J. (1996) Artificial neural networks as rainfall runoff models. Hydrol. Sci. J. 41(3), 399-417.
Olden, J.D. & Jackson, D.A. (2002) Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Modelling. 154, 135-150.
Özesmi, S.L. & Özesmi, U. (1999) An artificial neural network approach to spatial habitat modelling with interspecific interaction. Ecol. Modelling. 116, 15-31.
Rajurkar, M.P., Kothyari, U.C. & Chaube, U.C. (2002) Artificial neural networks for daily rainfall-runoff modelling. Hydrol. Sci. J. 47, 865-877.
Tokar, A.S. & Johnson, P.A. (1999) Rainfallrunoff modeling using artificial neural networks. J. Hydrol. Eng. ASCE. 4, 232- 239.
Wilby, R.L., Abrahart, R.J. & Dawson, C.W. (2003) Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48, 163- 181