Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications
The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.
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Application des réseaux de neurones artificiels pour le transport
sédimentaire en nappe," Hydrological Sciences Journal, vol. 47, pp.
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Resources, vol. 27, pp. 185-195, 2004.
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optimization algorithm for suspended sediment concentration prediction
and estimation," Hydrological Sciences Journal, vol. 49, pp. 1025-
1040, 2004.
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[1] S. K. Jain, "Development of integrated sediment rating curves using
ANNs," Journal of Hydraulic Engineering. ASCE, vol. 127, pp. 30-37,
2001.
[2] A. Ab. Ghani, N. E. M. Yahaya, R. Abdullah, and N. A. Zakaria,
"Development of Sediment Rating Curves for Pari River," Proceeding
of Rivers' 99, Universiti Sains Malaysia, pp. 284-290, 2002.
[3] H. M. Nagy, K. Watanabe, and M. Hirano, "Prediction of Sediment
Load Concentration in Rivers using Artificial Neural Network Model,"
Journal of Hydraulic Engineering, vol. 128, pp. 588-595, 2002.
[4] G. Tayfur, "Artificial neural networks for sheet sediment transport /
Application des réseaux de neurones artificiels pour le transport
sédimentaire en nappe," Hydrological Sciences Journal, vol. 47, pp.
879 - 892, 2002.
[5] S. K. Sinnakaudan, A. Ab. Ghani, M. S. S. Ahmad, and N. A. Zakaria,
"Flood Risk Mapping for Pari River Incorporating Sediment Transport,"
Journal of Environmental Modelling & Software, vol. 18, pp. 119-130,
2003.
[6] H. K. Cigizoglu, "Estimation and forecasting of daily suspended
sediment data by multi-layer perceptrons," Advances in Water
Resources, vol. 27, pp. 185-195, 2004.
[7] O. Kisi, "Multi-layer perceptrons with Levenberg-Marquardt
optimization algorithm for suspended sediment concentration prediction
and estimation," Hydrological Sciences Journal, vol. 49, pp. 1025-
1040, 2004.
[8] A. Agarwal, R. D. Singh, S. K. Mishra, and P. K. Bhunya, "ANN-based
sediment yield models for Vamsadhara river basin (India)," Water SA,
vol. 31, pp. 95-100, 2005.
[9] O. Kisi, "Suspended sediment estimation using neuro-fuzzy and neural
network approaches," Hydrological Sciences Journal, vol. 50, pp. 683-
696, 2005.
[10] B. Sivakumar and W. W. Wallender, "Predictability of river flow and
suspended sediment transport in the Mississippi River basin: a nonlinear
deterministic approach," Earth Surface Processes and
Landforms, vol. 30, pp. 665-677, 2005.
[11] H. K. Cigizoglu and M. Alp, "Generalized regression neural network in
modelling river sediment yield," Advances in Engineering Software,
vol. 37, pp. 63-68, 2006.
[12] H. K. Cigizoglu and Ö. Kisi, "Methods to improve the neural network
performance in suspended sediment estimation," Journal of Hydrology,
vol. 317, pp. 221-238, 2006.
[13] N. S. Raghuwanshi, R. Singh, and L. S. Reddy, "Runoff and Sediment
Yield Modeling Using Artificial Neural Networks: Upper Siwane River,
India," Journal of Hydrologic Engineering, vol. 11, pp. 71-79, 2006.
[14] M. Alp and H. K. Cigizoglu, "Suspended sediment load simulation by
two artificial neural network methods using hydrometeorological data,"
Environmental Modelling & Software, vol. 22, pp. 2-13, 2007.
[15] M. Ardıçlıoğlu, Ö. Kişi, and T. Haktanır, "Suspended sediment
prediction using two different feed-forward back-propagation
algorithms," Canadian Journal of Civil Engineering, vol. 34, pp. 120-
125, 2007/01/01 2007.
[16] A. K. Lohani, N. K. Goel, and K. K. S. Bhatia, "Deriving stage-
discharge-sediment concentration relationships using fuzzy logic,"
Hydrological Sciences Journal, vol. 52, pp. 793-807, 2007/08/01 2007.
[17] O. Kisi, "Constructing neural network sediment estimation models using
a data-driven algorithm," Math. Comput. Simul., vol. 79, pp. 94-103,
2008.
[18] O. Kisi, I. Yuksel, and E. Dogan, "Modelling daily suspended sediment
of rivers in Turkey using several data-driven techniques / Modélisation
de la charge journalière en matières en suspension dans des rivières
turques à l'aide de plusieurs techniques empiriques," Hydrological
Sciences Journal, vol. 53, pp. 1270-1285, 2008/12/01 2008.
[19] T. Partal and H. K. Cigizoglu, "Estimation and forecasting of daily
suspended sediment data using wavelet-neural networks," Journal of
Hydrology, vol. 358, pp. 317-331, 2008.
[20] R. Rai and B. Mathur, "Event-based Sediment Yield Modeling using
Artificial Neural Network," Water Resources Management, vol. 22, pp.
423-441, 2008.
[21] M. Cobaner, B. Unal, and O. Kisi, "Suspended sediment concentration
estimation by an adaptive neuro-fuzzy and neural network approaches
using hydro-meteorological data," Journal of Hydrology, vol. 367, pp.
52-61, 2009.
[22] V. Jothiprakash and V. Garg, "Reservoir Sedimentation Estimation
Using Artificial Neural Network," Journal of Hydrologic Engineering,
vol. 14, pp. 1035-1040, 2009.
[23] O. Kisi, T. Haktanir, M. Ardiclioglu, O. Ozturk, E. Yalcin, and S.
Uludag, "Adaptive neuro-fuzzy computing technique for suspended
sediment estimation," Advances in Engineering Software, vol. 40, pp.
438-444, 2009.
[24] T. Rajaee, S. A. Mirbagheri, M. Zounemat-Kermani, and V. Nourani,
"Daily suspended sediment concentration simulation using ANN and
neuro-fuzzy models," Science of The Total Environment, vol. 407, pp.
4916-4927, 2009.
[25] W.-C. Chou, "Modelling Watershed Scale Soil Loss Prediction and
Sediment Yield Estimation," Water Resources Management, vol. 24,
pp. 2075-2090, 2010.
[26] M. Firat and M. G├╝ngör, "Monthly total sediment forecasting using
adaptive neuro fuzzy inference system," Stochastic Environmental
Research and Risk Assessment, vol. 24, pp. 259-270, 2010.
[27] K. Özgür, "River suspended sediment concentration modeling using a
neural differential evolution approach," Journal of Hydrology, vol. 389,
pp. 227-235, 2010.
[28] O. M. Rezapour, L. T. Shui, and D. B. Ahmad, "Review of Artificial
Neural Network Model for Suspended Sediment Estimation," Australian
Journal of Basic and Applied Sciences, vol. 4, pp. 3347-3353, 2010.
[29] M. Talebizadeh, S. Morid, S. Ayyoubzadeh, and M. Ghasemzadeh,
"Uncertainty Analysis in Sediment Load Modeling Using ANN and
SWAT Model," Water Resources Management, vol. 24, pp. 1747-1761,
2010.
[30] B. C. van Maanen, G.; Bryan, K. R.; Ruessink, B. G., "The use of
artificial neural networks to analyze and predict alongshore sediment
transport," Nonlinear Processes in Geophysics, vol. 17, pp. 395-404,
2010.
[31] A. Guven and Ö. Kişi, "Estimation of Suspended Sediment Yield in
Natural Rivers Using Machine-coded Linear Genetic Programming,"
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@article{"International Journal of Architectural, Civil and Construction Sciences:49410", author = "M. R. Mustafa and M. H. Isa and R. B. Rezaur", title = "Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications", abstract = "The use of artificial neural network (ANN) modeling
for prediction and forecasting variables in water resources
engineering are being increasing rapidly. Infrastructural applications
of ANN in terms of selection of inputs, architecture of networks,
training algorithms, and selection of training parameters in different
types of neural networks used in water resources engineering have
been reported. ANN modeling conducted for water resources
engineering variables (river sediment and discharge) published in
high impact journals since 2002 to 2011 have been examined and
presented in this review. ANN is a vigorous technique to develop
immense relationship between the input and output variables, and
able to extract complex behavior between the water resources
variables such as river sediment and discharge. It can produce robust
prediction results for many of the water resources engineering
problems by appropriate learning from a set of examples. It is
important to have a good understanding of the input and output
variables from a statistical analysis of the data before network
modeling, which can facilitate to design an efficient network. An
appropriate training based ANN model is able to adopt the physical
understanding between the variables and may generate more effective
results than conventional prediction techniques.", keywords = "ANN, discharge, modeling, prediction, sediment,", volume = "6", number = "2", pages = "77-9", }
