Archivio Istituzionale della RicercaTwo nonlinear models, nonlinear prediction (NLP) and artificial neural networks (ANN), are compared for multivariate flood forecasting. For NLP the calibration of the locally linear model is quite simple, while for ANN the validation and identification of the model can be cumbersome, mainly because of overfitting. Very good results are obtained with the two methods: NLP performs slightly better at short forecast times while the situation is reversed for longer times.
A comparison of nonlinear flood forecasting methods / Laio, Francesco; Porporato, A.; Revelli, Roberto; Ridolfi, Luca. - In: WATER RESOURCES RESEARCH. - ISSN 0043-1397. - STAMPA. - 39(5):(2003). [10.1029/2002WR001551]
A comparison of nonlinear flood forecasting methods.
LAIO, FRANCESCO;REVELLI, Roberto;RIDOLFI, LUCA
2003
Abstract
Two nonlinear models, nonlinear prediction (NLP) and artificial neural networks (ANN), are compared for multivariate flood forecasting. For NLP the calibration of the locally linear model is quite simple, while for ANN the validation and identification of the model can be cumbersome, mainly because of overfitting. Very good results are obtained with the two methods: NLP performs slightly better at short forecast times while the situation is reversed for longer times.
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https://hdl.handle.net/11583/1404878
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