Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31097
Neural Networks: From Black Box towards Transparent Box Application to Evapotranspiration Modeling

Authors: A. Johannet, B. Vayssade, D. Bertin


Neural networks are well known for their ability to model non linear functions, but as statistical methods usually does, they use a no parametric approach thus, a priori knowledge is not obvious to be taken into account no more than the a posteriori knowledge. In order to deal with these problematics, an original way to encode the knowledge inside the architecture is proposed. This method is applied to the problem of the evapotranspiration inside karstic aquifer which is a problem of huge utility in order to deal with water resource.

Keywords: Hydrology, Neural-Networks, Evapotranpiration, Hidden Function Modeling

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1424


[1] Y. Oussar, G. Dreyfus. "How to be a Gray Box: Dynamic Semi-physical Modeling". Neural Networks, invited paper, vol. 14, 2001, pp. 1161-1172.
[2] A. Johannet, A. Mangin, D. D'Hulst. "Subterranean Water Infiltration Modelling by Neural Networks: Use of Water Source Flow". In Proc. of ICANN, M. Marinaro and P.G. Morasso eds, Springer, 1994, pp. 1033- 1036.
[3] G. Cybenko. "Approximation by Superposition of a Sigmoidal Function". Math. Ctrl Signal Syst, 2, 1989, pp. 293-342.
[4] H. Moradkhani, K. Hsu, H. V. Gupta, S. Sorooshian. "Improved Streamflow Forecasting Using Self-Organizing Radial Basis Function Artificial Neural Networks". Journal of Hydrology, 295, 2004, pp. 246- 262.
[5] I. N. Daliakopoulos, P. Coulibaly, I. K. Tsanis. "Groundwater Level Forecasting Using Artificial Neural Networks". Journal of Hydrology 309, 2005, pp. 229-240.
[6] D. I. Jeong, Y. O. Kim. "Rainfall-Runoff models using artificial neural networks for ensemble streamflow prediction". Hydrological Processes, 19, 2005, pp. 3819-3835.
[7] B. Kurtulus M. Rasac. "Evaluation of the ability of an artificial neural network model to simulate the input-outpout responses of a large karstic aquifer : the Larochefoucault aquifer (Charente - France)". Hydrogeological Journal, 2006.
[8] A. P. Jaquin, A. Y. Shamseldin. "Development of rainfall-runoff models using Takagi-Sugeno fuzzy inference systems". Journal of Hydrology, 329, 2006, pp. 145-173.
[9] A.-L. Courbis, E. Touraud and B. Vayssade. "Water balance diagnosis based on a simulation tool". ENVIROSOFT'98, 1998, pp. 199-208.
[10] A. Johannet, P-A. Ayral, B. Vayssade. "Modelling non Measurable Processes by Neural Networks: Forecasting Underground Flow Case Study of the Cèze Basin (Gard - France)". CISSE, 2006.
[11] D. Rumelhart, G. Hinton, R. Williams. "Learning Internal Representation by Error Propagation". PDP, MIT Press, 1988.
[12] E.A. Bender. "Mathematical Method for Artificial Intelligence". IEEE Computer Society Press, 1996.
[13] A.J. Shepherd. "Second-Order Methods for Neural Networks". Springer, 1997.
[14] D.W. Marquardt. Journal of the Society for Industrial and Applied Mathematics, vol 11, pp. 431-441.
[15] W.H. Press, S.A.Teukolsky, W.T. Vetterling, B.P. Flannery. "Numerical recipies in C". Cambridge University Press, 1992.
[16] Narendra K. S., Parthasarathy K. "Gradient Methods for the Optimization of Dynamical Systems Containning Neural Networks". IEEE trans. neur. net., vol 2, 1991, n┬░2, pp. 252-262.
[17] Werbos P.J. "Backpropagation Throught Time : What it Does and How to Do It". Proc. IEEE, 78, N┬░10, 1990, pp. 1550-1560.
[18] Mangin A. (1970). "Le système karstique du Baget (Ariège)". Annales de Spéléologie, vol 25, fasc. 3.
[19] J.E. Nash, J. V. Sutcliffe. "River Flow Forecasting through Conceptual Model. Part I - A Discussion of Principles". Journal of Hydrology, 10, 1970, pp. 282-290.
[20] L. Oudin et al. "Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 2 Towards a simple and efficient potential evapotranspiration model for rainfall-runoff modelling". Journal of hydrology, 303, 2005, pp. 290-306.
[21] Geman S. Bienenstock E. & Doursat R. "Neural networks and the bias/variance dilemma". Neural Computation 4, 1992, pp. 1-58.