Dependable flow modeling in upper basin Citarum using multilayer perceptron backpropagation

Ika Sari Damayanthi Sebayang(1*), Muhammad Fahmia(2),

(1) Mercu Buana University
(2) Mercu Buana University
(*) Corresponding Author


To determine the amount of dependable flow, a hydrological approach is needed where changes in rainfall become runoff. This diversification is a very complex hydrological phenomenon. Where this is a nonlinear process, with time changing and distributed separately. To approach this phenomenon, an analysis of the hydrological system has been developed using a model which is a simplification of the actual natural variables. The model is formed by a set of mathematical equations that reflect the behavior of parameters in hydrology. Modeling in this case uses artificial neural networks, multilayer perceptron combined with the backpropagation method is used to study the rainfall-runoff relationship and verify the model statistically based on the mean square error (MSE), Nash-Sutcliffe Efficiency (NSE) and correlation coefficient value (R2). Of the three models formed, model 3 provides optimum results with correlation levels using NSE per month as follows, in Cikapundung Sub-Basin NSE = 0,990703, R2 = 0,995008, and MSE = 0,00014443, while in Citarik Sub-Basin NSE = 0.9500, R2 = 0.97592, and MSE = 0.0010804 . From these results it can be seen that ANN has a fairly good ability to replicate random discharge fluctuations in the form of artificial models that have almost the same fluctuations and can also be applied in rainfall runoff modelization even though the results of the test results are not very accurate because there are still irregularities


Dependable Flow, Perceptron, Backpropagation, Citarum, Rainfall-runoff

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Soemarto, C.D., 1995, Hidrologi Teknik, Penerbit Erlangga, Jakarta, vi+316 hlm.

Bessaih, N., Mah, Y. S., Muhammad, S.M., Kuok, K.K., and Rosmina, A.B., “Artificial Neural Networks for Daily Runoff Simulation”, Faculty of Engineering, Universiti Malaysia Sarawak, 2003.

Junsawang, P., J. Asavanant, C. Lursinsap. 2007. “Artificial Neural Network Model for Rainfall-Runoff Relationship”, ASIMMOD, Chiang Mai, Thailand.

Rajurkar, M. P., Kothyari, U. C. & Chaube, U. C. (2004). Modeling of daily rainfall–runoff relationship with artificial neural network.

Abdulla, F. and L.A. Badranih. 2000. “Application of a Rainfall-Runoff Model to Three Catchments in Iraq”. Journal of Hydrological Sciences, 45: 13-25.

Ika Sari Damayanthi S. et. al, “Identification of Renewable Energy Potential in Ciberang River, Cisarua Village, Bogor, West Java”, IOP Conf. Ser.: Mater. Sci. Eng. 343 . April. 2018.

Setiawan, B.I. dan Rudiyanto, 2004. “Aplikasi Neural Networks Untuk Prediksi Aliran Sungai”, Prosiding Semiloka Teknologi Simulasi dan Komputasi serta Aplikasi 2004 – BPPT, Jakarta.

Kitanidis, P.K. and R.L. Bras. 1980a. “Adaptive Filtering Through Detection of Isolated Transient Errors In Rainfall-Runoff Models”. Water Resource Res., 16 (4): 740-748.


Hsu, K. L., Gupta, H. V. & Sorroshian, S. (1995) Artificial neural network modelling of the rainfall–runoff process. Water Resour. Res. 31(10), 2517–2530.

Jain, S. K., Das, A. & Srivastava, D. K. (1999) Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plan. Manage. ASCE 125(5), 263–271.

Lima, C. H. R. & Ferreira Filho, W. M. (2003) Análise de modelos de redes neurais aplicados ao processo chuva-deflúvio no semi-árido. XV Simpósio Brasileiro de Recursos Hídricos. Curitiba. Relação de trabalhos. Curitiba: Associação Brasileira de Recursos Hídricos, ABRH. CD ROM.

Machado, F. W. (2005) Modelagem chuva-vazão mensal utilizando redes neurais artificiais. MSc Thesis, Universidade Federal do Paraná, Curitiba, Paraná, Brazil.

Machado, F. W., Santos, I., Perreira Filho, D. L. B. & Mine, M. R. M. (2005) Avaliação do ajuste e extrapolação de curvas de descarga através de redes neurais. XX Congreso Nacional del Agua – CONAGUA & III Simposio de Recursos Hidricos del Cono Sur. Mendonza, Argentina.

Modarres R. (2009) Multi-criteria validation of artificial neural network rainfall–runoff modeling. Hydrol. Earth System Sci. 13(3), 411–421.

Muller, I. I. (1995) Métodos de avaliação da evaporação e evapotranspiração: análise comparativa para o Estado do Paraná. MSc Thesis, Universidade Federal do Paraná, Curitiba, Paraná, Brazil.

Neelakantan, T. R. & Pundarikanthan, N. V. (2000) Neural networkbased simulation-optimization model for reservoir operation. J.Water Resour. Plan. Manage. ASCE 126(2), 57–64.

Murray, R., Neumerkel, D., Sbarbaro, D., 1992, Neural Networks for Modeling and Control of A Non-Linear Dynamic System. Proceedings of the 1992 IEEE International Symposium on Intelligent Control, Glasgow, Scotland, pp. 404–409.

Rumelhart, E., G. Hinton and R. Williams, 1986. “Learning Internal Representations by Error Propagation”.Parallel Distributive Process, 1: 218-362.

Wilby, R.L., R.J. Abrahart, and C.W. Dawson. 2003. “Detection Of Conceptual Model Rainfall-Runoff Processes Inside An Artificial Neural Network”, Hydrol. Sci. J., 48 (2): 163-181.

Jain, A., K.P. Sudheer and Srinivasulu, S. 2004. “Identification Of Physical Processes Inherent In Artificial Neural Network Rainfall-Runoff Models”. Hydro. Process, 118 (3): 571-581.

Sudheer, K.P. and A. Jain. 2004. “Explaining The Internal Behavior of Artificial Neural Network River Flow Models”. Hydrol. Process, 118 (4): 833-844.

N. J. de Vos and T. H. M. Rientjes,“ Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation,” Hydrology and Earth System Sciences’. pp.111-126. July, 2005.

Asaad Y. Shamseldin,“ Artificial neural network model for river flow forecasting in a developing country,” Journal of Hydroinformatics’. doi: 10.2166/hydro.2010.027. 2010.

Kuok King Kuok and Nabil Bessaih,“ Artificial Neural Networks (ANNS) For Daily Rainfall Runoff Modeling,”The Institution of Engineers’. Vol. 68, No.3. September, 2007.

Yonas B. Dibike and Dimitri P. Solomatine, “River Flow Forecasting Using Artificial Neural Networks,” EGS journal of Physics and Chemistry of the Earth’. MS-No. EGS1.1-99002.



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