Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

• Autorzy: Bouzeria H. , Ghenim A. N. , Khanchoul K.

Identyfikatory

DOI

10.1515/jwld-2017-0018

Warianty tytułu

PL

Zastosowanie sztucznych sieci neuronowych do przewidywania ładunku zawiesiny; przypadek zlewni rzeki Mellah w północno-wschodniej Algierii

• Języki publikacji: EN

Abstrakty

EN

In this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R²) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

PL

W niniejszej pracy przedstawiono działanie najlepszego algorytmu sieci neuronowych z użyciem wielowarstwowego perceptronu do przewidywania odpływu zawiesiny ze zlewni rzeki Mellah. Do treningu i testowania sieci użyto serii czasowych dobowego odpływu zawiesiny i odpływu wody z profilu wodowskazowego Bouchegouf. Do oceny działania modelu wykorzystano szereg parametrów statystycznych, takich jak pierwiastek ze średniego błędu kwadratowego, średni błąd bezwzględny, współczynnik wydajności i współczynnik determinacji. Model dawał zadowalające wyniki i wykazywał bardzo dobrą zgodność między obserwowanymi i przewidywanymi danymi. Wyniki świadczą także, że model jest w stanie wychwycić szczegółowy wzorzec odpływu zawiesiny ze zlewni rzeki Mellah.

Słowa kluczowe

EN

ANN model; discharge; Mellah catchment; MLP; north east of Algeria; prediction; sediment

PL

modele sztucznych sieci neuronowych (ANN); odpływ; północno-wschodnia Algieria; przewidywanie; zawiesina; zlewnia Mellah

• Wydawca: Wydawnictwo ITP
• Czasopismo: Journal of Water and Land Development
• Rocznik: 2017
• Tom: no. 33
• Strony: 47--55
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Bouzeria H.

• University of Tlemcen, Faculty of Technology, Department of Hydraulics, BP 230 Chetouane Tlemcen 13000, Algeria

autor

Ghenim A. N.

• University of Tlemcen, Faculty of Technology, Department of Hydraulics, BP 230 Chetouane Tlemcen 13000, Algeria

autor

Khanchoul K.

• University of Annaba, Soils and Sustainable Development Laboratory, Annaba, Algeria

Bibliografia

• ALP M., CIGIZOGLU H.K. 2007. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling and Software. Vol. 22. Iss. 1 p. 2–13.
• ARDIÇLIOĞLU M., KIŞI Ö., HAKTANIR T. 2007. Suspended sediment prediction by using two different feed-forward backpropagation algorithms. Canadian Journal of Civil Engineering. Vol. 34. Iss. 1 120–125.
• CHANG C.K., AZAMATHULLA H.M., ZAKARIA N.A., GHANI A.A. 2012. Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers. Journal of Earth System Science. Vol. 121. Iss. 1 p. 125–133.
• CIGIZOGLU H.K. 2004. Estimation and forecasting of daily suspended sediment data by multi-layer perceptrons. Advances in Water Resources. Vol. 27 p. 185–195.
• CIGIZOGLU H.K. 2006. Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software. Vol. 37 p. 63–68.
• CIGIZOGLU H.K., KISI O. 2006. Methods to improve the neural network performance in suspended sediment estimation. Journal of Hydrology. Vol. 317 p. 221–238.
• COBANER M. 2009. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of Hydrology. Vol. 367 p. 52–61.
• DAWSON C.W., WILBY R.L. 2001. Hydrological modelling using artificial neural networks. Progress in Physical Geography. Vol. 25. Iss. 1 p. 80–108.
• DEMIRCI M., ÜLNEŞ F., SAYDEMIR S. 2015. Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83–95.
• JAIN S.K. 2001. Development of integrated sediment rating curves using ANNs. Journal of Hydraulic Engineering. Vol. 127. Iss. 1 p. 30–37.
• KAKAEI LAFDANI E.K., MOGHADDAM NIA A.M., AHMADI A. 2013. Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology. Vol. 478 p. 50–62.
• KHANCHOUL K., TOURKI M., LE BISSONNAIS Y. 2015. Assessment of the artificial neural networks to geomorphic modelling of sediment yield for ungauged catchments, Algeria. Journal of Urban and Environmental Engineering (JUEE). Vol. 8. No. 2 p. 175–185.
• KISI Ö. 2004. Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation. Hydrological Sciences Journal. Vol. 49. Iss. 6 p. 1025–1040.
• KISI Ö. 2005. Suspended sediment estimation using neurofuzzy and neural network approaches. Hydrological Sciences Journal. Vol. 50. Iss. 4 p. 683–696.
• KISI Ö. 2008. Constructing neural network sediment estimation models using a data-driven algorithm. Mathematics and Computers in Simulation. Vol. 79 p. 94–103.
• KISI Ö. 2010. River suspended sediment concentration modeling using a neural differential evolution approach. Journal of Hydrology. Vol. 389 p. 227–235.
• KISI Ö., OZKAN C., AKAY B. 2012. Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm. Journal of Hydrology. Vol. 428–429 p. 94–103.
• KISI Ö., YUKSEL I., DOGAN E. 2008. Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques. Hydrological Sciences Journal. Vol. 53. Iss. 6 p. 1270–1285.
• LIU Q.J., SHI Z.H., FANG N.F., ZHU H.D., AI L. 2013. Modeling the daily suspended sediment concentration in a Raihyperconcentrated river on the loess Plateau, China, using the wavelet-ANN approach. Geomorphology. Vol. 186 p. 181–190.
• MAIER H.R. 2010. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling and Software. Vol. 25 p. 891–909.
• MELESSE A.M., AHMAD S., MCCLAIN M.E., WANG X., LIM Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management. Vol. 98. Iss. 5 p. 855–866.
• MUSTAFA M.R., ISA M.H. 2014. Comparative study of MLP and RBF neural networks for estimation of suspended sediments in Pari River, Perak. Research Journal of Applied Sciences, Engineering and Technology. Vol. 7. Iss. 18 p. 3837–3841.
• MUSTAFA M.R., ISA M.H., REZAUR R.B. 2011. A comparison of artificial neural networks for prediction of suspended sediment discharge in river – A case study in Malaysia. World Academy of Science, Engineering and Technology. Vol. 57 p. 372–376
• MUSTAFA M.R., REZAUR R.B., SAIEDI S., ISA M.H. 2012. River suspended sediment prediction using various multilayer perceptron neural network training algorithms: A case study in Malaysia. Water Resources Management. Vol. 26. Iss. 7 p. 1879–1897.
• NAGY H.M., WATANABE K., HIRANO M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model. Journal of Hydraulic Engineering. Vol. 128. Iss. 6 p. 588–595.
• OLYAIE E., BANEJAD H., CHAU K.W., MELESSE A.M. 2015. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environmental Monitoring and Assessment. Vol. 187. Iss. 4 p. 1–22.
• RAI R.K., MATHUR B.S. 2008. Event-based sediment yield modeling using artificial neural network. Water Resources Management. Vol. 22 p. 423–441.
• RAJAEE T. 2009. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Science of the Total Environment. Vol. 407 p. 4916–4927.
• RAJAEE T., MIRBAGHERI S.A., ZOUNEMAT-KERMANI M., NOURANI V. 2011. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Science of the Total Environment. Vol. 409 p. 2917–2928.
• SAHOO G.B., RAY C., MEHNERT E., KEEFER D.A. 2006. Application of artificial neural networks to assess pesticide contamination in shallow groundwater. Science of the Total Environment. Vol. 367 p. 234–251.
• SARANGI A. 2005. Comparison of Artificial Neural Network and regression models for sediment loss prediction from Banha watershed in India. Agricultural Water Management. Vol. 78 p. 195–208.
• TAYFUR G. 2002. Artificial neural networks for sheet sediment transport. Hydrological Sciences Journal. Vol. 47. Iss. 6 p. 879–892.
• TAYFUR G., GULDAL V. 2006. Artificial neural networks for estimating daily total suspended sediment in natural streams. Hydrology Research. Vol. 37. Iss. 1 p. 69–79.
• WANG W.C., CHAU K.W., CHENG C.T., QIU L.A. 2009. Comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology. Vol. 374 p. 294–306.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).