This paper proposes a new procedure for river stage forecasting under uncertainty based on the use of artificial neural networks whose parameters are represented by fuzzy numbers. These fuzzy parameters are estimated using a calibration procedure that imposes a constraint whereby for any assigned h-level (level of credibility) the envelope of the corresponding intervals representing the river stages forecasted at different time instants must include a preselected percentage of observed values. The application of the fuzzy neural network to a real case study and the comparison of the results with those provided by Bayesian neural networks shows the validity of the proposed procedure.
Fuzzy neural networks for river stage forecasting with uncertainty
ALVISI, Stefano;FRANCHINI, Marco
2010
Abstract
This paper proposes a new procedure for river stage forecasting under uncertainty based on the use of artificial neural networks whose parameters are represented by fuzzy numbers. These fuzzy parameters are estimated using a calibration procedure that imposes a constraint whereby for any assigned h-level (level of credibility) the envelope of the corresponding intervals representing the river stages forecasted at different time instants must include a preselected percentage of observed values. The application of the fuzzy neural network to a real case study and the comparison of the results with those provided by Bayesian neural networks shows the validity of the proposed procedure.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.