ÁLVAREZ, J. y S. BOLADO (1996) Descripción de los procesos de infiltración mediante redes neurales artificiales. Ingeniería del Agua, 3: 39-46.
BOX, G.E.P. y G.M. JENKINS (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco, CA.
CYBENCO, G. (1989) Approximation by superpositions of a sigmoidal function. Math. Controls, Signals, and Systems, 2: 303-314.
[+]
ÁLVAREZ, J. y S. BOLADO (1996) Descripción de los procesos de infiltración mediante redes neurales artificiales. Ingeniería del Agua, 3: 39-46.
BOX, G.E.P. y G.M. JENKINS (1976) Time series analysis: forecasting and control. Holden-Day, San Francisco, CA.
CYBENCO, G. (1989) Approximation by superpositions of a sigmoidal function. Math. Controls, Signals, and Systems, 2: 303-314.
COULBECK, B., S.T. TENNANT y C.H. ORR (1985) Development of a demand prediction program for use in optimal control of water supply. Systems Sci., 11: 59-66.
FRENCH, M.N., W.F. KRAJEWSKI y R.R. CUYKENDAL I (1992) Rainfall forecasting in space and time using a neural network. J. Hydrol., 137: 1-31.
GRIÑÓ, R. (1992) Neural networks for univariate time series forecasting and their application to water demand prediction. Neural Network World, 437-450.
GUTIÉRREZ-ESTRADA, J.C., I. PULIDO-CALVO y J. PRENDA (2000) Gonadosomatic index estimates of an introduced pumpkinseed (Lepomis gibbosus) population in a Mediterranean stream, using computational neural networks. Aquat. Sci., 62: 350-363.
HAIR, JR., J.F., R.E. ANDERSON, R.L. TATHAM y W.C. BLACK (1999) Análisis de regresión múltiple. En Análisis multivariante. Pretince Hall Iberia, 5ª ed., Madrid, 4: 143-226.
HARTLEY, J.A. y R.S. POWELL (1991) The development of a combined demand prediction system. Civ. Engrg. Systems, 8: 231-236.
HSU, K., H.V. GUPTA y S. SOROOSHIAN (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res., 31: 2517-2530.
JOWITT, P.W. y C. XU (1992) Demand forecasting for wáter distribution systems. Civ. Engrg. Systems, 9: 105-121.
KITANIDIS, P.K. y R.L. BRAS (1980) Real time forecasting with a conceptual hydrological model. 2. Applications and results. Water Resour. Res., 16: 1034-1044.
LEÓN, C., S. MARTÍN, J.M. ELENA y J. LUQUE (2000) EXPLORE-Hybrid expert system for water networks management. J. Water Resour. Planning and Mgmt., 126: 65-74.
MAIDMENT, D.R., S.P. MIAOU y M.M. CRAWFORD (1985) Transfer function models for daily urban water use. Water Resour. Res., 21: 425-432.
MINAI, A.A. y R.D. WILLIAMS (1990) Acceleration of back-propagation through learning rate and momentum adaptation. Int. Joint Conf. Neural Networks, 1: 676-679.
MOLINO, B., G. RASULO y L. TAGLIALATELA (1996) Forecast model of water consumption for Naples. Water Resour. Mgmt., 10: 321-332.
MOREU, P. (1999) Series temporales. En Estadística Informatizada. Paraninfo, 11: 93-108.
NEL, D. y J. HAARHOFF (1996) Sizing municipal water storage tanks with Monte Carlo simulation. J. Water SRTæAqua, 45: 203-212.
PULIDO-CALVO, I. (2001) Diseño y gestión óptimos de sistemas de impulsión y de almacenamiento de agua para riego. Tesis Doctoral, Dpto. de Agronomía, Universidad de Córdoba.
RAMAN, H. y V. CHANDRAMOULI (1996) Deriving a general operating policy for reservoirs using neural networks. J. Water Resour. Plng. and Mgmt., 122: 342-347.
RANJITHAN, S., J.W. EHEART y J.H. GARRET JR. (1993) Neural network-based screening for groundwater reclamation under uncertainty. Water Resour. Res., 29: 563-574.
RIZZO, D.M. y D.E. DOUGHERTY (1994) Characterization of aquifer properties using artificial neural networks: neural kriging. Water Resour. Res., 30: 483-497.
RÜFENATCH, H.P. y H. GUIBENTIF (1997) A model for forecasting water consumption in Geneva canton, Switzerland. J. Water SRTæAqua, 46: 196-201.
RUMELHART, D.E., G.E. HINTON y R.J. Willians (1986) 'Learning' representations by backpropagation errors. Nature, 323: 533-536.
SAPORTA, D. y M. MUÑOZ (1994) El consumo en redes de distribución. Predicción diaria de la demanda. En Mejora del rendimiento y de la fiabilidad en sistemas de distribución de agua. Aguas de Valencia y U.D. Mecánica de Fluidos (UPV), 2: 43-75.
SHVARTSER, L., U. SHAMIR y M. FELDMAN (1993) Forecasting hourly water demands by pattern recognition approach. J. Water Resour. Planning and Mgmt., 119: 611-627.
THIRUMALAIAH, K. y M.C. DEO (2000) Hydrological forecasting using neural networks. J. Hydrol. Engrg., 5: 180-189.
VENTURA, S., M. SILVA, D. PÉREZ-BENDITO y C. HERVÁS (1995) Artificial neural networks for estimation of kinetic analytical parameters. Anal. Chem., 67: 1521-1525.
VENTURA, S., M. SILVA, D. PÉREZ-BENDITO y C. HERVÁS (1997) Computational neural networks in conjuction with principal component analysis for resolving highly nonlinear kinetics. J. Chem. Inform. and Computer Sci., 37: 287-291.
YANG, C.C., S.O. PRASHER, R. LACROIX, S. SREEKANTH, N.K. PATNI y L. MASSE (1997) Artificial neural network model for subsurface-drained farmlands. J. Irrig. And Drain. Engrg., 123: 285-292.
[-]