A comparison of nonlinear flood forecasting methods

F. Laio, A. Porporato, R. Revelli, L. Ridolfi

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)TNN21-TNN24
JournalWater Resources Research
Issue number5
StatePublished - May 2003
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology


  • Artificial neural networks
  • Flood forecasting
  • Nonlinear prediction
  • Overfitting


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