743
Views
21
CrossRef citations to date
0
Altmetric
Original Articles

Are artificial neural network techniques relevant for the estimation of longitudinal dispersion coefficient in rivers? / Les techniques de réseaux de neurones artificiels sont-elles pertinentes pour estimer le coefficient de dispersion longitudinale en rivières?

Page 187 | Published online: 15 Dec 2009

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (4)

Azadeh Gholami, Hossein Bonakdari, Amir Hossein Zaji, Salma Ajeel Fenjan & Ali Akbar Akhtari. (2016) Design of modified structure multi-layer perceptron networks based on decision trees for the prediction of flow parameters in 90° open-channel bends. Engineering Applications of Computational Fluid Mechanics 10:1, pages 193-208.
Read now
AdamP. Piotrowski, JaroslawJ. Napiorkowski, PawelM. Rowinski & SteveG. Wallis. (2011) Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents. Hydrological Sciences Journal 56:5, pages 883-894.
Read now
DEG-HYO BAE, DAE MYUNG JEONG & GWANGSEOB KIM. (2007) Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrological Sciences Journal 52:1, pages 99-113.
Read now

Articles from other publishers (17)

Byunguk Kim, Siyoon Kwon, Hyoseob Noh & Il Won Seo. (2022) Surrogate prediction of the breakthrough curve of solute transport in rivers using its reach length dependence. Journal of Contaminant Hydrology 249, pages 104024.
Crossref
Azadeh Gholami, Hossein Bonakdari, Amir Hossein Zaji & Ali Akbar Akhtari. (2019) A comparison of artificial intelligence-based classification techniques in predicting flow variables in sharp curved channels. Engineering with Computers 36:1, pages 295-324.
Crossref
Monika Barbara Kalinowska. (2019) Effect of water–air heat transfer on the spread of thermal pollution in rivers. Acta Geophysica 67:2, pages 597-619.
Crossref
Akram Seifi & Hossien Riahi-Madvar. (2018) Improving one-dimensional pollution dispersion modeling in rivers using ANFIS and ANN-based GA optimized models. Environmental Science and Pollution Research 26:1, pages 867-885.
Crossref
Samad Emamgholizadeh, Kiana Bahman, S. Mohyeddin Bateni, Hadi Ghorbani, Isa Marofpoor & Jeffrey R. Nielson. (2016) Estimation of soil dispersivity using soft computing approaches. Neural Computing and Applications 28:S1, pages 207-216.
Crossref
Azadeh Gholami, Hossein Bonakdari, Amir Hossein Zaji, David G. Michelson & Ali Akbar Akhtari. (2016) Improving the performance of multi-layer perceptron and radial basis function models with a decision tree model to predict flow variables in a sharp 90° bend. Applied Soft Computing 48, pages 563-583.
Crossref
Abdusselam Altunkaynak. (2016) Prediction of longitudinal dispersion coefficient in natural streams by prediction map. Journal of Hydro-environment Research 12, pages 105-116.
Crossref
Vassilis Z. Antonopoulos, Pantazis E. Georgiou & Zissis V. Antonopoulos. (2015) Dispersion Coefficient Prediction Using Empirical Models and ANNs. Environmental Processes 2:2, pages 379-394.
Crossref
Z. Fuat Toprak, Nizamettin Hamidi, Ozgur Kisi & Reşit Gerger. (2013) Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods. KSCE Journal of Civil Engineering 18:2, pages 718-730.
Crossref
Robert J. Bialik, Jarosław J. Napiórkowski, Paweł M. Rowiński & Witold G. Strupczewski. 2014. Achievements, History and Challenges in Geophysics. Achievements, History and Challenges in Geophysics 109 125 .
Marcello Benedini & George TsakirisMarcello Benedini & George Tsakiris. 2013. Water Quality Modelling for Rivers and Streams. Water Quality Modelling for Rivers and Streams 49 56 .
M. B. Kalinowska & P. M. Rowiński. (2012) Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study. Hydrology and Earth System Sciences 16:11, pages 4177-4190.
Crossref
Adam P. Piotrowski, Pawel M. Rowinski & Jaroslaw J. Napiorkowski. (2012) Comparison of evolutionary computation techniques for noise injected neural network training to estimate longitudinal dispersion coefficients in rivers. Expert Systems with Applications 39:1, pages 1354-1361.
Crossref
Adam P. Piotrowski & Jarosław J. Napiorkowski. (2011) Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach. Journal of Hydrology 407:1-4, pages 12-27.
Crossref
Wei Zhang. (2011) A 2‐D numerical simulation study on longitudinal solute transport and longitudinal dispersion coefficient. Water Resources Research 47:7.
Crossref
Z. Fuat Toprak & Hikmet Kerem Cigizoglu. (2008) Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods. Hydrological Processes 22:20, pages 4106-4129.
Crossref
Z. Fuat Toprak & M. Emin Savci. (2007) Longitudinal Dispersion Coefficient Modeling in Natural Channels using Fuzzy Logic. CLEAN – Soil, Air, Water 35:6, pages 626-637.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.