A comparison of nonlinear flood forecasting methods

被引：51

作者：

Laio, F ^{[1
]}

Porporato, A ^{[1
]}

Revelli, R ^{[1
]}

Ridolfi, L ^{[1
]}

机构：

[1] Politecn Torino, Dept Hydraul Transport & Civil Infrastruct, I-10129 Turin, Italy

来源：

WATER RESOURCES RESEARCH | 2003年 / 39卷 / 05期

关键词：

flood forecasting; nonlinear prediction; artificial neural networks; overfitting;

D O I：

10.1029/2002WR001551

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：TNN21 / TNN24

页数：4

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