✦ LIBER ✦
Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selection
✍ Scribed by Paulin Coulibaly; Bernard Bobée; François Anctil
- Publisher
- John Wiley and Sons
- Year
- 2001
- Tongue
- English
- Weight
- 89 KB
- Volume
- 15
- Category
- Article
- ISSN
- 0885-6087
- DOI
- 10.1002/hyp.445
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root‐mean‐square error (RMSE) or the conventional Nash–Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd.