Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions-Reference-Cited by-同舟云学术

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Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions

Published:2006-06 Issue:3 Volume:51 Page:502-523
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

GIUSTOLISI ORAZIO¹,SIMEONE VINCENZO²

Affiliation:

1. a Department of Civil and Environmental Engineering , Technical University of Bari , Via Orabona 4, I-70125, Bari, Italy E-mail:

2. b Department of Engineering for Sustainable Development , Technical University of Bari , Via Turismo 8, Paolo VI, I-74100, Taranto, Italy E-mail:

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1623/hysj.51.3.502

Reference41 articles.

1. Managing uncertainty in hydrological models using complementary models

2. Using pruning algorithms and genetic algorithms to optimise network architectures and forecasting inputs in a neural network rainfall-runoff model

3. Artificial Neural Networks in Hydrology. II: Hydrologic Applications

4. Artificial neural network approach to flood forecasting in the River Arno

5. Modelling and forecasting of hydrological variables using artificial neural networks: the Kafue River sub-basin

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