Application of Artificial Neural Network in Water Resources Engineering: A Review

C. Gajendran*, P. Tamarai**, C. Mahendran***
* Assistant Professor, Department of Civil Engineering, Karunya University, Coimbatore, Tamil Nadu, India.
** Assistant Professor, Department of Civil Engineering, Government College of Engineering, Salem, Tamil Nadu, India.
*** Assistant Professor, Electronics and Communication Engineering, Government College of Engineering, Tirunelveli, Tamil Nadu, India.
Periodicity:May - July'2010
DOI : https://doi.org/10.26634/jfet.5.4.1191

Abstract

Water is vital for all aspects of human and ecosystem survival and health. Lack of water resources and optimum management has been two recent challenges of water resources engineering. Population growth, decrease of useable water resources, improvements in  lifestyle, growing rate of consumption, climate change and several other parameters have caused useable water to be a significant problem for the future. American Society of Civil Engineers (ASCE) research committee reported that Artificial Neural Network (ANN) is able to simulate many of complicated nonlinear processes.  Over the last few years, the use of artificial neural networks (ANNs) has increased in many areas of engineering. In particular, ANNs are being applied to many water resources engineering problems and have been demonstrated some degree of success.  A review of literature reveals that ANNs have been used successfully in water for quality and quantity prediction, modelling of water pollution and so on. The objective of this paper is to provide a general review of some ANN applications for solving some types of water resources engineering problems.  It is not intended to describe the issue of ANNs modelling in water resources engineering and not covering every single application or scientific paper that is found in literature. However, some important works are selected to be described in some detail, while others are acknowledged for reference purposes.  The paper then discusses the strengths and limitations of ANNs compared with the other modelling approaches.

Keywords

ANN, GIS, Statistical Study, Regression Analysis, Fuzzy Logic, Water Resources Engineering.

How to Cite this Article?

Gajendran, C.,Thamarai, P., and Mahendran , C. (2010). Application Of Artificial Neural Network In Water Resources Engineering: A Review. i-manager’s Journal on Future Engineering and Technology, 5(4), 1-6. https://doi.org/10.26634/jfet.5.4.1191

References

[1]. Bertoni, J. C., Tucci, C. E., and Clarke, R. T. (1992). “Rainfall-based real-time flood forecasting” J. Hydrology, Vol. 131, pp.313-339.
[2]. Campolo, M., Andreussi, P., and Soldati, A. (1999). “A river flood forecasting with a neural network model”. Water Resource Research, Vol. 35(4), pp.1191-1197.
[3]. Campolo, M., Soldati, A., and Andreussi, P. (2003). “Artificial neural network approach to flood forecasting in the River Arno.” Journal of Hydrology and Science, Vol. 48(3), pp.381-398.
[4]. Cigizoglu, H.K. (2002). “Filling missing suspended sediment data by artificial neural networks:, Int. Conf. on Computational Methods in Water Resources, Netherlands, Elsevier, pp.1645-1652.
[5]. Cigizoglu, H.K. (2003). “Estimation, forecasting and extrapolation of acceleration data by artificial neural networks”, Journal of Hydrology and Science, Vol. 48(3), pp.349-361.
[6]. Elshorbagy A, Simonovic, S P, and Panu, U.S. (2002). “Estimation of missing stream flow data using principles of chaos theory”, Journal of Hydrology, Vol. 255, pp.123- 133.
[7]. Fernando DAK, and Jayawardena A.W. (1998). “Runoff forecasting using RBF networks with OLS algorithm”, Journal of Hydrology and Engineering, Vol. 3(3), pp.203-209.
[8]. Freiwan, M. and Cigizoglu, H.K. (2005). “Prediction of total monthly rainfall in Jordan using feed forward back propagation method” Fresenius Environ Bull, Vol. 14(2), pp.142-51.
[9]. Govindaraju, R. S. (2000a). Artificial neural network in hydrology, I: Preliminary concepts. Journal of Hydrologic Engineering, Vol. 5(2), pp.115-123.
[10]. Govindaraju, R. S. (2000b). Artificial neural network in hydrology, II: Hydrological applications. Journal of Hydrologic Engineering, Vol. 5(2), pp.124-137.
[11]. Hall, M.J, Minns, A.W. (1998), “Regional flood frequency analysis using Artificial Neural Networks”, Hydro informatics conference, Denmark, Rotterdam, p. 759- 763.
[12]. Hawkin,S . (1994) . Neural networks -A comprehensive foundation, Macmillan college publishing company, NY.
[13]. Imrie, C. E., Durucan, S., and Korre, A. (2000). “River flow prediction using artificial neural networks: Generalization beyond the calibration range”. Journal of Hydrology, Vol. 233, pp.138-153
[14]. Khalil M, Panu US, Lennox WC. (2001). Groups and neural networks based streamflow data infilling procedures. Journal of Hydrol, 241, pp.153-176.
[15]. Kheir El-Din KA. (1998). Neural network application for modelling hydraulic characteristics of severe construction. In, editors Babovic V, Larsen CL, Hydro nd informatics conference. Proc. 2 vol., third international conference on hydro informatics, Copenhagen, Denmark. Rotterdam: A.A. Balkema, pp.771-775.
[16]. Kim, G. S., and Borros, A.P. (2001). “Quantitative flood forecasting using multi sensor data and neural networks”. Journal of Hydrology, 246, pp.45-62.
[17]. Lange, N. (1998). “Advantages of unit hydrograph derivation by neural networks. Hydro informatics conference. Denmark. Rotterdam, pp.783-789
[18]. Liong, S.Y., Lim, W. and Paudyal, G. N. (2000). “River stage forecasting in Bangladesh: Neural network approach”. Journal of Computation in Civ. Eng, Vol. 14(1), pp.1-18.
[19]. McCulloch, W.S., and Pitts W. (1943). “A logical calculus of the ideas imminent in nervous activity,” Bulletin and Mathematical Biophysics, Vol. 5, pp.115-133
[20]. Metcalf Eddy. (1995). Wastewater Engineering, Treatment, Disposal and Reuse, 5 Edition, McGraw Hill, NY.
[21]. Minns AW, Hall MJ. (1996). “Artificial neural networks as rainfall runoff models” HydrolSci Journal, Vol. 41(3), pp.9-417.
[22]. Nasseri, M., Asghari, K., and Abedinid, M.J. (2008). ”Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network”, Expert Systems with Applications, Vol. 35, pp.1415-1421.
[23]. Navone, H. D., and Ceccatto, H. A. (1994). “Predicting Indian monsoon rainfall: A neural network approach”, Climate Dynamics, Vol. 10, pp.305-312.
[24]. Rajurkar M. P., and Chaube U. C. (2002), “Artificial neural networks for daily rainfall-runoff modeling”, Journal of Hydrology Science, Vol. 47 (6), pp. 865–877.
[25]. Ray, C., Klindworth, K.K. (2000). “Neural networks for agricultural vulnerability assessment of rural private wells”, Journal of Hydrology and Engineering, Vol. 5 (2), pp.162- 171.
[26]. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). ”Learning internal representations by error propagation” In: Rumelhart, D.E., McClelland, J.L. (Eds.), Parallel Distributed Processing. MIT Press, Cambridge.
[27]. Sawyer, C. N., McCarty, P. L., and Parkin, G. F. (1994).”Chemistry for Environmental Engineering”, McGraw-Hill International Editions.
[28]. Shamseldin, A.Y. (1997). “Application of neural network technique to rainfall-runoff modeling”, Journal of Hydrology, Vol. 199, pp.272-294.
[29]. Shin, H. S., and Salas J.D. (2000). “Regional drought analysis based on neural networks”, Journal of Hydrology and Engineering, Vol. 5(2), pp.145-155.
[30]. Smith J., and Eli R.N. (1995). “Neural-network models of rainfall runoff process.” Jr. Water Resource. Plan. Manage.,Vol. 121(6), pp.499–508.
[31].Thandaveswara, B.S., Sajikumar, N. (2000). “Classification of river basins using artificial neural network”. Journal of Hydrology and Engineering, Vol. 5(3), pp.290-298.
[32].Thirumalaiah, K., and Deo, M.C. (2000). “Hydrological forecasting using artificial neural networks”, Journal of Hydrologic Engineering, Vol. 5(2), pp.180-189.
[33]. Thirumalaiah, K., and Deo, M.C. (1998). “River stage forecasting using artificial neural networks”, Journal of Hydrologic Engineering, Vol. 3(1), pp.26-31.
[34]. Tokar, A. S., and Markus, M. (2000). “Precipitationrunoff modeling using artificial neural networks and conceptual models”, Journal of Hydrologic and Engineering, Vol. 5(2), 156-161.
[35]. Tokar, AS., and Johnson, P.A. (1999). “Rainfall–runoff modelling using artificial neural networks”, Journal of Hydrology and Engineering, Vol. 4(3), pp.232-239.
[36]. Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). “Short term stream flow forecasting using artificial neural networks”, Journal of Hydrology, Vol. 214, pp.32- 48.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.