Learning time reduction in artificial neural networks applied to hydrology;Disminución de los tiempos de entrenamiento en redes neuronales artificiales aplicadas a hidrología

Ingenieria Hidraulica en Mexico

Volumn 18, Issue 2, 2003, Pages 69-82

  Brahm, Alfredo B ^a   Varas E C , Eduardo C ^a  

^a PONTIFICIA UNIVERSIDAD CATÓLICA DE CHILE   (Chile)

Author keywords

Artificial neural networks; Flow prediction; Learning algorithms; Principal component analysis; Rainfall forecasting; Water resources systems

Indexed keywords

ALGORITHMS; HYDROLOGY; PRINCIPAL COMPONENT ANALYSIS; RAIN;

TIME REDUCTION;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; LEARNING; PRINCIPAL COMPONENT ANALYSIS; RAINFALL; RUNOFF;

ARGENTINA; UNITED STATES;

EID: 0037592748     PISSN: 01864076     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (8)

1. BISHOP, C.M. Neural networks for pattern recognition. Reino Unido: Oxford University Press, 1999, 482 pp.
2. JACKSON, J.E. A user's guide to principal component. New York: Ed. Wiley, 1991, 569 pp.
3. FRENCH, M.N., KRAJEWSKI, W.F. y CUYKENDALL, R.R. Rainfall forecasting in space and time using a neural network. Journal of hydrology. Núm. 137, 1992, pp. 1-31.
4. HAGAN, D.B. Neural network design. Boston: PWS Publishing Company, 1995, 736 pp.
5. LINSLEY, R. The relation between rainfall intensity and topography in northern California. California (EUA): Research Report 1. Department of Civil Engineering, Stanford University, 1956, 29 pp.
6. RUSELL, D.R. y MARKS, R.J. II neural smoothing, supervised learning in feed forward artificial neural networks. Cambridge (EUA): MIT Press, 1990, 352 pp.
7. DÖLLING, O. Sistemas de apoyo a la gestión integral de cuencas hidrográficas. Chile: tesis de doctorado. Escuela de Ingeniería, Pontificia Universidad Católica de Chile, 2001, 201 pp.
8. ZURADA, J.M. Introduction to artificial neural systems. New York: West Publishing Co., 1992, 785 pp.