Top

Water Resources Management

Published in:

01-05-2012

River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia

Authors: M. R. Mustafa, R. B. Rezaur, S. Saiedi, M. H. Isa

Published in: Water Resources Management | Issue 7/2012

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Estimation of suspended sediment discharge in rivers has a vital role in dealing with water resources problems and hydraulic structures. In this study, a Multilayer Perceptron (MLP) feed forward neural network with four different training algorithms was used to predict the suspended sediment discharge of a river (Pari River at Silibin) in Peninsular Malaysia. The training algorithms are Gradient Descent (GD), Gradient Descent with Momentum (GDM), Scaled Conjugate Gradient (SCG), and Levenberg Marquardt (LM). Different statistical measures, time of convergence and number of epochs to reach the required accuracy were used to evaluate the performance of training algorithms. The analysis showed that SCG and LM performed better than GD and GDM. While the performance of the superior algorithms (i.e., SCG and LM) is similar, LM required considerably shorter time of convergence. It was concluded that both training algorithms SCG and LM could be recommended for suspended sediment prediction using MLP networks. However, LM was the faster (1/7 of SCG convergence time) of the two algorithms.

previous article The Cost of Irrigation Water Delivery: An Attempt to Reconcile the Concepts of Cost and Efficiency

next article Hydrologic and Hydrogeologic Characterization of a Deltaic Aquifer System in Orissa, Eastern India

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Agarwal A, Rai RK, Upadhyay A (2009) Forecasting of runoff and sediment yield using Artificial Neural Networks. J Water Resour Prot 1:368–375CrossRef

Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydro-meteorological data. Environ Modell Softw 22(1):2–13CrossRef

Bhadra A, Bandyopadhyay A, Singh R, Raghuwanshi N (2010) Rainfall-runoff modeling: comparison of two approaches with different data requirements. Water Resour Manag 24:37–62CrossRef

Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, 482

Caudill M (1987) Neural Networks Primer, Part I. AI Expert 2(12):46–52, December 1987

Chou W-C (2010) Modelling watershed scale soil loss prediction and sediment yield estimation. Water Resour Manag 24:2075–2090CrossRef

Cigizoglu HK (2004) Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Adv Water Resour 27(2):185–195CrossRef

Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37(2):63–68CrossRef

Cigizoglu KH, Kisi O (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317(3–4):221–238CrossRef

Darus A, Ab Ghani, A Zakaria NA, Abdullah R (2003) Evaluation of Alluvial River stability for river restoration: case study of Raia River and Pari River. Proceedings of the East-Asia Regional Seminar on River Restoration, Kuala Lumpur, 13th–15th January

Dawson CW, Harpham C, Wilby RL, Chen Y (2002) Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China. Hydrol Earth Syst Sci 6(4):619–626CrossRef

Deift P, Zhou X (1993) A steepest descent method for oscillatory Riemann-Hilbert problems Asymptotics for the MKdV equation. Ann Math 137(2):295–368CrossRef

Demirel MC, Anabela V, Kahya E (2009) Flow forecast by SWAT model and ANN in Pracana Basin, Portugal. Adv Eng Softw 40:467–473CrossRef

DID (1988) Hydrological data: Streamflow and river suspended sediment records. Ministry of Agriculture, Malaysia

Edossa D, Babel M (2011) Application of ANN-based streamflow forecasting model for agricultural water management in the Awash River Basin, Ethiopia. Water Resour Manag 25:1759–1773CrossRef

Evsukoff A, Lima B, Ebecken N (2011) Long-term runoff modeling using rainfall forecasts with application to the Iguaçu River Basin. Water Resour Manag 25:963–985CrossRef

Fernando DAK, Shamseldin A (2009) Investigation of internal functioning of the radial basis function neural network river flow forecasting models. J Hydrol Eng ASCE 14(3):286–292CrossRef

Guven A, Kişi Ö (2011) Estimation of suspended sediment yield in natural rivers using machine-coded linear genetic programming. Water Resour Manag 25:691–704CrossRef

Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5(6):989–993CrossRef

Jayawardena AW, Fernando DAK (1998) Use of radial basis function type artificial neural network for runoff simulation. Comput Aided Civ Infrastruct Eng 3(2):91–99CrossRef

Jothiprakash V, Garg V (2009) Reservoir sedimentation estimation using artificial neural network. J Hydrol Eng 14(9):1035–1040CrossRef

Ju Q, Yu Z, Hao Z, Ou G, Zhao J, Liu D (2009) Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72:2873–2883CrossRef

Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50(4):693–696

Kisi O (2008) Constructing neural network sediment estimation models using a data-driven algorithm. Math Comput Simulat 79(1):94–103CrossRef

Kisi O, Haktanir T (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40(6):438–444CrossRef

Kisi O, Nia A, Gosheh M, Tajabadi M, Ahmadi A (2011) Intermittent streamflow forecasting by using several data driven techniques. Water Resour Manag 1–18

Krause P, Byole DP, Base F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97CrossRef

Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ Model Softw 15:101–124CrossRef

Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resources variables in river systems: Current status and future directions. Environ Model Softw 25:891–909CrossRef

May DB, Sivakumar M (2009) Prediction of urban storm water quality using artificial neural networks. Environ Model Softw 24:296–302CrossRef

Melesse AM, Ahmad S, McClain ME, Wang X, Lim YH (2011) Suspended sediment load prediction of river systems: an artificial neural network approach. Agric Water Manag 98(5):855–866CrossRef

Mohamed A, Ouillon S (2007) Suspended sediment transport in a semiarid watershed, Wadi Abd, Algeria (1973–1995). J Hydrol 343(3–4):187–202

Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533CrossRef

Nagy HM, Watanabe K, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128(6):588–595CrossRef

Panagoulia D (2006) Artificial neural networks and high and low flows in various climate regimes. Hydrol Sci J 51(4):563–587CrossRef

Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12:145–151CrossRef

Qin M, Richard HJ, Yuan Z, Mark WJ, Bo S (2007) The effects of sediment-laden waters on irrigated lands along the lower Yellow River in China. J Environ Manag 85(4):858–865CrossRef

Rafik Z, Ridha B, Daniel R (2008) Levenberg-Marqurdt learning neural network for adaptive pre-distortion for time varying HPA with memory in OFDM systems. 16th European Signal Processing Conference, August 25–29, Lausanne, Switzerland

Rojas R (1996) Neural networks: A systematic introduction. Springer Verlag, Berlin, pp 151–184

Samseldin AY (1997) Application of neural network technique to rainfall runoff modeling. J Hydrol 199:272–294CrossRef

Schraudolph NN, Graepel T (2002) Towards stochastic conjugate gradient methods. In proceedings of the 9th International conference on neural information processing ICONIP 2002. Piscataway, NJ, USA: IEEE, 853–856

Sinnakaudan SK, Ab Ghani A, Ahmad MSS, Zakaria NA (2003) Flood risk mapping for Pari River incorporating sediment transport. J Environ Modell Softw 18(2):119–130CrossRef

Sinnakaudan SK, Ab Ghani A, Ahmad MSS, Zakaria NA (2006) Multiple linear regression model for total bed material load prediction. J Hydraul Eng 132(5):521–528CrossRef

Smith J, Eli RN (1995) Neural network models of rainfall runoff process. J Water Resour Plan Manag ASCE 121(6):499–580CrossRef

Talebizadeh M, Morid S, Ayyoubzadeh SA, Ghasemzadeh M (2010) Uncertainty analysis in sediment load modeling using ANN and SWAT model. Water Resour Manag 24:1747–1761CrossRef

Tawfik M, Ibharim A, Fahmy H (1997) Hysteresis sensitive Neural Network for modeling rating curves. J Comput Civil Eng ASCE 11(3):206–211CrossRef

Tayfur G (2002) Artificial neural networks for sheet sediment transport/Application des réseaux de neurones artificiels pour le transport sédimentaire en nappe. Hydrol Sci J 47(6):879–892CrossRef

Wang Y, Kim S, Principe JC (2005) Comparison of TDNN training algorithms in brain machine interfaces. International Joint Conference on Neural Networks, IJCNN2005

Zhu YM, Lu XX, Zhou Y (2007) Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang River in the Upper Yangtze Catchment, China. Geomorphology 84:111–125CrossRef

Title: River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia
Authors: M. R. Mustafa
R. B. Rezaur
S. Saiedi
M. H. Isa
Publication date: 01-05-2012
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 7/2012
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-012-9992-5

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 7/2012

Integrated Modeling of Global Change Impacts on Agriculture and Groundwater Resources

Simulating the Impact of Pricing Policies on Residential Water Demand: A Southern France Case Study

Optimizing Structural Best Management Practices Using SWAT and Genetic Algorithm to Improve Water Quality Goals

Evaluation of Water Balance in a Mountainous Upland Catchment Using SEBAL Approach

Managing Adaptation of Urban Water Systems in a Changing Climate

Evaluating Water Transfer Projects Using Analytic Network Process (ANP)