Abstract
This paper presents modeling of artificial neural network (ANN) to forecast the suspended sediment discharges (SSD) during flood events in two different catchments in the Seybouse basin, northeastern Algeria. This study was carried out on hourly SSD and water discharge data during flood events from a period of 31 years in the Ressoul catchment and of 28 years in the Mellah catchment. The ANNs were trained according to two different algorithms: the Levenberg–Marquardt algorithm (LM) and the Quasi-Newton algorithm (BFGS). Seven input combinations were trained for the SSD prediction. The performance results indicated that both algorithms provided satisfactory simulations according to the determination coefficient (R2) and root mean squared error (RMSE) performance criteria, with priority to the BFGS algorithm; the coefficient of determination using the LM algorithm varies between 51.0 and 90.2%, whereas using the BFGS algorithm it varies between 54.3 and 93.5% in both studied catchments, with calculated improvement for all seven developed networks with the best improvement in the Ressoul catchment presented in ANN06 with \(\Delta_{{R^{2} }}\) 4.23% and \(\Delta_{{{\text{RMSE}}}}\) 1.74‰, and with the best improvement presented in ANN05 with \(\Delta_{{R^{2} }}\) 6.07% and \(\Delta_{{{\text{RMSE}}}}\) 0.71‰ in the Mellah catchment. The analysis showed that the use of Quasi-Newton method performed better than the Levenberg–Marquardt in both studied areas.
Similar content being viewed by others
References
Afan HA, El-Shafie A, Yaseen ZM, Hameed MM, Mohtar WHMW, Hussain A (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245
Ahmed F, Hassan M, Hashmi HN (2018) Developing nonlinear models for sediment load estimation in an irrigation canal. Acta Geophys 66:1485–1494
Alizadeh MJ, Nodoushan EJ, Kalarestaghi N, Chau KW (2017) Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environ Sci Pollut Res 24:28017–28025
Alp M, Cigizoglu HK (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model softw 22:2–13
Bae DH, Jeong DM, Kim G (2007) Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrol Sci J 52:99–113
Bouguerra H, Bouanani A, Khanchoul K, Derdous O, Tachi SE (2017) Mapping erosion prone areas in the Bouhamdane watershed (Algeria) using the Revised Universal Soil Loss Equation through GIS. J Water Land Dev 32:13–23
Bouhadeb CHE, Menani MR, Bouguerra H, Derdous O (2018) Assessing soil loss using GIS based RUSLE methodology, case of the Bou Namoussa watershed – North-East of Algeria. J Water Land Dev 36:27–35
Bouzeria H, Ghenim AN, Khanchoul K (2017) Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria. J Water Land Dev 33:47–55
Broyden CG (1970) The convergence of single-rank quasi-newton methods. Math Comput 24:365–382
Clair TA, Ehrman JM (1998) Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon and nitrogen export in eastern Canadian rivers. Water Resour Res 34:447–455
Dennis J, More J (1977) Quasi-Newton methods, motivation and theory. SIAM Rev Soc Ind Appl Math 19:46–89
Fletcher R (1970) A new approach to variable metric algorithms. Comput J 13:317–322
Goldfarb D (1970) A family of variable-metric methods derived by variational means. Math Comput 24:23–26
Golob R, Štokelj T, Grgič D (1998) Neural-network-based water inflow forecasting. Control Eng Pract 6:593–600
Hagan MT, Menhaj MB (1994) Training multilayer networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:989–993
Halff AH, Halff HM, Azmoodeh M (1993) Predicting runoff from rainfall using neural networks. In: Engineering hydrology. ASCE, pp 760–765
Hassan M, Shamim MA, Sikandar A, Mehmood I, Ahmed I, Ashiq SZ, Khitab A (2015) Development of sediment load estimation models by using artificial neural networking techniques. Environ Monit Assess 187(11):686
Hassan M, Zaffar H, Mehmood I, Khitab A (2018) Development of streamflow prediction models for a weir using ANN and step-wise regression. Model Earth Syst Environ 4:1021–1028
Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plan Manag 125:263–271
Kisi Ö (2004) Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrol Sci J 49:1025–1040
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 98:855–866
Mustafa MR, Rezaur RB, Saiedi S, Isa MH (2012) River suspended sediment prediction using various multilayer perceptron neural network training algorithms—a case study in Malaysia. Water Resour Manage 26:1879–1897
Nagy HM, Watanabe KAND, Hirano M (2002) Prediction of sediment load concentration in rivers using artificial neural network model. J Hydraul Eng 128:588–595
Partal T (2009) River flow forecasting using different artificial neural network algorithms and wavelet transform. Can J Civ Eng 36:26–39
Piasecki A, Jurasz J, Adamowski JF (2018) Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method. Acta Geophys 66:1093–1107
Riad S, Mania J, Bouchaou L, Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Math Comput Model 40:839–846
Rowinski PM, Czernuszenko W (1998) Experimental study of river turbulence under unsteady conditions. Acta Geophysica Polonica 46:461–480
Sahoo GB, Ray C, De-Carlo EH (2006) Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu. Hawaii J Hydrol 327:525–538
Shamim MA, Hassan M, Ahmad S, Zeeshan M (2016) A Comparison of artificial neural networks (ANN) and local linear regression (LLR) techniques for predicting monthly reservoir levels. KSCE J Civ Eng 20:971–977
Shanno DF (1970) Conditioning of Quasi-Newton methods for function minimization. Math Comput 24:647–656
Sudheer KP, Jain A (2004) Explaining the internal behaviour of artificial neural network river flow models. Hydrol Process 18:833–844
Tachi SE, Ouerdachi L, Remaoun M, Derdous O, Boutaghane H (2016) Forecasting suspended sediment load using regularized neural network: case study of the Isser River (Algeria). J Water Land Dev 29:75–81
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306
Wilamowski BM, Yu H (2010) Improved computation for Levenberg–Marquardt training. IEEE Trans Neural Netw 21:930–937
Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214:32–48
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 84:111–125
Zou R, Lung WS, Wu J (2007) An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling. Water Resour Res 43:W08427
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bouguerra, H., Tachi, SE., Derdous, O. et al. Suspended sediment discharge modeling during flood events using two different artificial neural network algorithms. Acta Geophys. 67, 1649–1660 (2019). https://doi.org/10.1007/s11600-019-00373-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11600-019-00373-4