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Issue
1 January - Volume 10, Issue 1, Pages 1 - 111
1 March - Volume 10, Issue 2, Pages 113 - 188
1 May - Volume 10, Issue 3, Pages 189 - 265
1 October - Volume 10, Issue 4, Pages 267 - 345
Volume 10, Issue 3
1 May 2008
ISSN 1464-7141
EISSN 1465-1734
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In this Issue
Research Articles
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Research Articles
Uncertainty of model parameters in stream pollution using fuzzy arithmetic
H. Mpimpas
;
P. Anagnostopoulos
;
J. Ganoulis
Abstract
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A holistic approach and object-oriented framework for eco-hydraulic simulation in coastal engineering
P. Milbradt
;
T. Schonert
Abstract
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PDF
Volume-based inverse model (VINMOD): innovative approach to quantify groundwater pollution in heterogeneous aquifers
Allelign Zeru
;
Gerhard Schäfer
Abstract
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PDF
Numerical analysis of coupled hydrosystems based on an object-oriented compartment approach
Olaf Kolditz
;
Jens-Olaf Delfs
;
Claudius Bürger
;
Martin Beinhorn
;
Chan-Hee Park
Abstract
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Numerical modelling of heavy metals for riverine systems using a new approach to the source term in the ADE
A. Roshanfekr
;
S. M. Kashefipour
;
N. Jafarzadeh
Abstract
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PDF
Honey-bee mating optimization (HBMO) algorithm in deriving optimal operation rules for reservoirs
O. Bozorg Haddad
;
A. Afshar
;
M. A. Mariño
Abstract
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PDF
Erratum
Development of pipe deterioration models for water distribution systems using EPR
L. Berardi
;
Z. Kapelan
;
O. Giustolisi
;
D. A. Savic
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Finite volume model for the simulation of 1D unsteady river flow and water quality based on the WASP
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A reduced-order model for the regeneration of surface currents in Gorgan Bay, Iran
by Hassan Kheirabadi, Roohollah Noori, Jamal M. V. Samani, Jan Franklin Adamowski, Mohammad Hassan Ranjbar, Naser Hajizadeh Zaker, 2018 Journal of Hydroinformatics 20(6), 1419–1435, https://doi.org/10.2166/hydro.2018.149
A multi-model integration method for monthly streamflow prediction: modified stacking ensemble strategy
Self-adaptive multi-population based Jaya algorithm to optimize the cropping pattern under a constraint environment
Prediction of sediment transport rates in gravel-bed rivers using Gaussian process regression
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