Prediction of Discharge in a Tidal River Using Artificial Neural Networks-Reference-Cited by-同舟云学术

Prediction of Discharge in a Tidal River Using Artificial Neural Networks

Published:2014-08 Issue:8 Volume:19 Page:04014006
ISSN:1084-0699
Container-title:Journal of Hydrologic Engineering
language:en
Short-container-title:J. Hydrol. Eng.

Author:

Hidayat H.¹,Hoitink A. J. F.²,Sassi M. G.³,Torfs P. J. J. F.⁴

Affiliation:

1. Researcher, Hydrology and Quantitative Water Management Group, Wageningen Univ., 6708PB Wageningen, Netherlands; and Research Centre for Limnology, Indonesian Institute of Sciences, 16911 Cibinong, Indonesia (corresponding author).

2. Associate Professor, Hydrology and Quantitative Water Management Group, Wageningen Univ., 6708PB Wageningen, Netherlands.

3. Researcher, Hydrology and Quantitative Water Management Group, Wageningen Univ., 6708PB Wageningen, Netherlands.

4. Assistant Professor, Hydrology and Quantitative Water Management Group, Wageningen Univ., 6708PB Wageningen, Netherlands.

Publisher

American Society of Civil Engineers (ASCE)

Subject

General Environmental Science,Water Science and Technology,Civil and Structural Engineering,Environmental Chemistry

Link

http://ascelibrary.org/doi/pdf/10.1061/%28ASCE%29HE.1943-5584.0000970

Reference37 articles.

1. Neural network modelling of non-linear hydrological relationships

2. Neural networks and M5 model trees in modelling water level–discharge relationship

3. Subtidal water level variation controlled by river flow and tides

4. Integration of artificial neural networks with conceptual models in rainfall-runoff modeling

5. Comparison of ANN approach with 2D and 3D hydrodynamic models for simulating estuary water stage

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