Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models

This publication has 39 references indexed in Scilit:

• Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments
  Journal of Hydrology, 2001
• Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
  Published by Elsevier ,2000
• GR3J: a daily watershed model with three free parameters
  Hydrological Sciences Journal, 1999
• Neural-network-based d-step-ahead predictors for nonlinear systems with time delay
  Engineering Applications of Artificial Intelligence, 1999
• An artificial neural network approach to rainfall-runoff modelling
  Hydrological Sciences Journal, 1998
• Artificial neural networks as rainfall-runoff models
  Hydrological Sciences Journal, 1996
• Automatic calibration of conceptual rainfall-runoff models: sensitivity to calibration data
  Journal of Hydrology, 1996
• Selection of calibration objective fonctions in the context of rainfall-ronoff modelling in a Sudanese savannah area
  Hydrological Sciences Journal, 1991
• Sensitivity of optimized parameters in watershed models
  Water Resources Research, 1978
• River flow forecasting through conceptual models part I — A discussion of principles
  Journal of Hydrology, 1970