Abstract
Predicting the streamflow of rivers can have a significant economic impact, as this can help in agricultural water management and in providing protection from water shortages and possible flood damage. In this study, two statistical models have been used; Deseasonalized Autoregressive moving average model (DARMA) and Artificial Neural Network (ANN) to predict monthly streamflow which important for reservoir operation policy using different time scale, monthly and 1/3 monthly (ten-days) flow data for River Nile basin at five key stations. The streamflow series is deseasonalized at different time scale and then an appropriate nonseasonal stochastic DARMA (p, q) models are built by using the plots of Partial Auto Correlation Function (PACF) to determine the order (p) of DARMA model. Then the deseasonalized data for key stations are used as input to ANN models with lags equals to the order (p) of DARMA model. The performance of ANN and DARMA models are compared using statistical methods. The results show that the developed model (using 1/3 monthly (ten-days) and ANN) has the best performance to predict monthly streamflow at all key stations. The results also show that the relative error in the developed model result did not exceed 9% while in the traditional models reach to 68% in the flood months in the testing period. The result also indicates that ANN has considerable potential for river flow forecasting.
Similar content being viewed by others
References
Abrahart, R.J., Kneale, P.E., and See, L.M., Neural Networks for Hydrological Modeling, Leiden: A.A. Balkema publishers, 2004.
Abrahart, R.J., See, L., and Kneale, P.E., Investigating the role of saliency analysis with a neural network rainfall-runoff model, Comput. Geosci., 2001, vol. 27, pp. 921–928.
Adamowski, J., Peak daily water demand forecast modeling using artificial neural networks, J. Water Resour. Plan. Manage., 2008, vol. 134, no. 2, pp. 119–128. doi 10.1061/(ASCE)0733–9496(2008)134:2(19)
Adamowski, J. and Chan, H.F., A wavelet neural network conjunction model for groundwater level forecasting, J. Hydrol., 2011, vol. 407, pp. 28–40. doi 10.1016/j.jhydrol.2011.06.013
Ahmed, A., Elshafie A., Karim, O., and Jaffar, O., Evaluation the efficiency of Radial Basis Function Neural Network for Prediction of water quality parameters, Eng. Intelligent Syst., 2009, vol. 17, no. 4, pp. 221–231.
Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., and Lallahem, S., River flow model using artificial neural networks, Energy Procedia, 2015, vol. 74, pp. 1007–1014.
Bender, M. and Simonovic, S., Time-series modeling for long-range stream-flow forecasting, J. Water Res. Planning and Manag., 1994, vol. 120, no. 6, pp. 857–870. doi 10.1061/(ASCE)0733–9496(1994)120:6(857)
Biswas, R.K. and Jayawardena, A.W., Water Level Prediction by Artificial Neural Network in A Flashy Transboundary River of Bangladesh, Global NEST J., 2014, vol. 16, no. 2, pp. 432–444.
Box, G.E. and Jenkins, G.M., Time Series Analysis, Forecasting and Control, Revised edition, Holden-Day, 1976.
Chow, V.T., Maidment, D.R., and Mays, L.W., Applied Hydrology, N.Y.: McGraw-Hill Book Company, 1988.
Dashora, L., Singal, S.K., and Srivastav, D.K., Software Application for Data Driven Prediction Models for Intermittent Streamflow for Narmada River Basin, Int. J. Computer Applic., 2015, vol. 113, no. 10.
Dawson, C.W. and Wilby, R.L., Hydrological modeling using artificial neural networks, Progress in Phys. Geogr., 2001, vol. 25, pp. 80–108.
Dibike, Y.B. and Solomatine, D.P., River flow forecasting using artificial neural networks, Phys. Chem. Earth, Pt B: Hydrol., Oceans and Atmos., 2001, vol. 26, no. 1, pp. 1–7. doi 10.1016/S1464–1909(01)85005-X
Elganiny, M. A. and Eldwer, A.E, Comparison of stochastic models in forecasting monthly streamflow in rivers: a case study of River Nile and its tributaries, J. Water Resour. Prot., 2016, pp. 143–153.
Elganiny, M. A. and Eldwer, A.E., Stochastic forecasting models of the monthly streamflow for the Blue Nile at Eldiem Station, Water Resour., 2005, vol. 45, no. 3, pp. 326–337, 2018.
El-Shafie, A., Najah, A.A., and Karim, O., Application of neural network for scour and air entrainment prediction, Int. Conf. Computer Technol. Development, 2009, pp. 273–277.
El-Shafie, A. and Noureldin, A., Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam, Hydrol. Earth Syst. Sci. Discussions, 2010, vol. 7, no. 5, pp. 7957–7993.
El-Shafie, A., Alaa, E.A., Noureldin, A., and Mohd, R.T., Enhancing Inflow Forecasting Model at Aswan High Dam Utilizing Radial Basis Neural Network and Upstream Monitoring Stations Measurements, Water Resour. Manag., 2009, vol. 23, no. 11, pp. 2289–2315.
El-Shafie, A., Mukhlisin, M., Najah, A., and Taha, M.R., Performance of artificial neural network and regression techniques for rainfall-runoff prediction, Int. J. Math., Phys. Eng. Sci., 2011, vol. 6, no. 8, pp. 1997–2003.
El-Shafie, A., Noureldin, A., Taha, M.R., and Basri, H., Neural network model for Nile River inflow forecasting based on correlation analysis of historical inflow data, J. Appl. Sci., 2008, vol. 8, no. 24, pp. 4487–4499.
El-Shafie, A., Reda, T.M., and Noureldin, A., A Neuro-Fuzzy Model for Inflow Forecasting of the Nile River at Aswan High Dam, Water Reours. Manag., 2007, vol. 21, no. 3, pp. 533–556.
Ghanbarpour, M.R., Abbaspour, K.C., Jalalvand, G., and Moghaddam, G.A., Stochastic modeling of surface stream flow at different time scales: Sangsoorakh karst basin, Iran, J. Cave Karst Stud., 2007, vol. 72, no. 1, pp. 1–10. doi 10.4311/jcks2007ES0017
Ghumman, A.R., Ghazaw, Y.M., Sohail, A.R., and Watanabe, K., Runoff forecasting by artificial neural network and conventional model, Alexandria Eng. J., 2011, vol. 50, pp. 345–350.
Hamilton, J.D., Time Series Analysis, Princeton Univ. Press, 1994.
Hapuarachchi, H. and Zhijia, L., Application of models with different types of modeling methodologies for river flow forecasting, Weather Radar Inf. Distributed Hydrol. Modeling, 2003, pp. 218–226.
Haykin, S., Neural Networks: Comprehensive Foundation. Upper Saddle River, N.J, USA: Prentice-Hall, 1999.
Hipel, K.W. and McLeod, A.I., Time Series Modeling of Water Resources and Environmental Systems, Amsterdam: Elsevier Sci., 1994.
Hsu, K.L., Gupta, H.V., Gao, X., and Sorooshian, S., Estimation of physical variables from multichannel remotely sensed imagery using a neural network: application to rainfall estimation, Water Resour. Res., 1999, vol. 35, no. 5, pp. 1605–1618.
Huang, W., Xu, B., and Chan-Hilton, A., Forecasting flows in Apalachicola River using neural networks, Hydrol. Processes, 2004, vol. 18, no. 13, pp. 2545–2564. doi 10.1002/hyp.1492
Karunanithi, N., Grenney, W.J., Whitley, D., and Bovee, K., Neural networks for river flow prediction, J. Comput. Civ. Eng., 1994, vol. 8, no. 2, pp. 201–220.
Kim, S.J., Hyun, Y., and Lee, K.K., Time series modeling for evaluation of groundwater discharge rates into an urban subway system, Geosci. J., 2005, vol. 9, no. 1, pp. 15–22.
Kisi, O., Streamflow forecasting using different artificial neural network algorithms, ASCE J. Hydrol. Eng., 2007, vol. 12, no. 5, pp. 532–539.
Kumar, A.P.S., Sudheer, K.P., Jain, S.K., and Agarwal, P.K., Rainfall-runoff modeling using artificial neural networks: comparison of network types, Hydrol. Processes, 2005, vol. 19, pp. 1277–1291.
Lin, G.F. and Chen, L.H., Application of an artificial neural network to typhoon rainfall forecasting, Hydrol. Process, 2005, vol. 19, pp. 1825–37.
Luk, K.C., Ball, J.E., and Sharma, A., An application of artificial neural networks for rainfall forecasting, Math. Comput. Modell., 2001, vol. 33, pp. 683–693.
Maier, H.R. and Dandy, G.C., The effect of internal parameters and geometry on the performance of backpropagation neural networks: an empirical study, Environ. Model Softw., 1998, vol. 13, no. 2, pp. 193–209.
Martins, O.Y., Sadeeq, M.A., and Ahaneku, I.E., ARMA modelling of Benue River flow dynamics: comparative study of PAR model, Open J. Mod. Hydrol., 2011, vol. 1, pp. 1–9.
McKerchar, A.I. and Delleur, J.W., Application of seasonal parametric linear stochastic models to monthly flow data, Water Resour. Res., 1974, vol. 10, no. 2, pp. 246–255.
More, J.J., The Levenberg–Marquardt algorithm: implementation and theory, numerical analysis, Lecture Notes in Mathematics, Watson, G.A., Ed., N.Y.: Springer, 1977, vol. 630, pp. 105–116.
Mustafa, M.R., Isa, M.H., and Rezaur, R.B., Artificial Neural Networks Modeling in Water Resources Engineering: Infrastructure and Applications, Int. J. Civil, Environ., Struct., Constr. Architect. Eng., 2012, vol. 6, no. 2.
Najah, A., Elshafie, A., Karim, O.A., and Jaffar, O., Prediction of Johor River water quality parameters using artificial neural networks, Eur. J. Sci. Res., 2009, vol. 28, no. 3, pp. 422–435.
Nash, J.E. and Sutcliffe, J.V., River flow forecasting through conceptual models: Part I. A discussion of principles, J. Hydrol., 1970, vol. 10, pp. 282–290.
Nayak, P.C., Sudheer, K.P., and Ramasastri, K.S., Fuzzy computing based rainfall runoff model for real time flood forecasting, Hydrol. Process., 2005, vol. 19, no. 4.
Noakes, D.J., McLeod, A.I., and Hipel, K.W., Forecasting monthly riverflow time series, Int. J. Forecasting, 1985, vol. 1, no. 2, pp. 179–190. doi 10.1016/0169–2070(85)90022–6
Nourani, V., Using Artificial Neural Networks (ANNs) for sediment Load Forecasting of Talkherood River Mouth, J. Urban and Environ. Engineering, 2009, vol. 3, no. 1, pp. 1–6.
Pramanik, N. and Panda, R.K., Application of neural network and adaptive neuro fuzzy inference systems for stream flow prediction, Hydrol. Sci. J., 2009, vol. 54, no. 2, pp. 247–260. doi 10.1623/hysj.54.2.247
Rabindra, K.P., Niranjan, P., and Biplab, B., Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model, J.Comput. Geosci., 2010, vol. 36, no. 6, pp. 735–745.
Rahsepar, M. and Mahmoodi, H., Predicting Weekly Discharge Using Artificial Neural Network (ANN) Optimized by Artificial Bee Colony (ABC) Algorithm: A Case Study, Civil Engin. Urban Plan.: An Int. J. (CiVEJ), 2014, vol. 1, no. 1.
Rezaeian, Z.M., Amin, S., Khalili, D., and Singh, V.P., Daily outflow prediction by multi-layer perceptron with logistic sigmoid and tangent sigmoid activation functions, Water Resour Manag., 2010, vol. 24, no. 11, pp. 2673–2688.
Rezaeianzadeh, M., Stein, A., Tabari, H., Abghari, H., Jalalkamali, N., Hosseinipour, E., and Singh, V., Assessment of a conceptual hydrological model and artificial neural networks for daily outflows forecasting, Int. J. Environ. Sci. Technol., 2013. doi 10.1007/s13762–013–0209–0
Riad, S., Mania, J., Bouchaou, L., and Najjar, Y., Rainfall-Runoff Model Using an Artificial Neural Network Approach, Math. Computer Modelling, 2004, vol. 40, pp. 839–846.
Rumelhart, D.E., Hinton, G.E., and Williams, R.J., Learning representations by backpropagating errors, Nature, 1986, vol. 323, pp. 533–536.
Salas, J.D., Analysis and modeling of hydrologic time series, in Handbook of Hydrol., Maidment, D.R., Ed., N.Y.: McGraw-Hill, 1992.
Shahin, M., Hydrology of the Nile Basin, N.Y.: Elsevier Sci. Publ., 1985.
Shalamu, A., Chun, C., King, J.P., and Abudukadeer, K., Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China, Water Sci. Eng., 2010, vol. 3, no. 3, pp. 269–281. doi 10.3882/j.issn.1674-2370.2010. 03.003
Shrestha, R.R. and Nestmann, F., Physically based and data-driven models and propagation of input uncertainties in river flood prediction, J. Hydrol. Eng., 2009, vol. 14, pp. 1309–1319.
Singh, S.K., Jain, S.K., and Brdossy, A., Training of artificial neural networks using information-rich data, Hydrol., 2014, vol. 1, pp. 40–62. doi 10.3390/hydrology1010040
Solomatine, D.P. and Dulal, K.N., Model trees as an alternative to neural networks in rainfall–runoff modeling, Hydrol. Sci. J., 2003, vol. 48, pp. 399–411.
Sudheer, K.P., Gosain, A.K., and Ramasastri, K.S., A data driven algorithm for constructing artificial neural network rainfall-runoff models, Hydrol. Process., 2002, vol. 16, no. 6, pp. 1325–1330.
Tawfik, M., Linearity versus non-linearity in forecasting Nile River flows, Adv. Eng. Software, 2003, vol. 34, pp. 515–524.
Thirumalaiah, K. and Deo, M.C., Hydrological forecasting using neural networks, J. Hydrol. Eng., 2000, vol. 5, pp. 180–189.
Veiga, V.B., Hassan, Q.K., and He, J., Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada, Water, 2015, vol. 7, pp. 99–115. doi 10.3390/w7010099
Victor, B.V., Quazi, K.H., and Jianxun, H., Development of Flow Forecasting Models in the Bow River at Calgary, Alberta, Canada, Water, 2015, vol. 7, pp. 99–115. doi 10.3390/w7010099
Wang, W., Stochasticity, Nonlinearity and Forecasting of Streamflow Processes, Amsterdam: IOS Press, 2006.
Yonaba, H., Anctil, F., and Fortin, V., Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting, ASCE J. Hydrol. Eng., 2010, vol. 15, no. 4, pp. 275–283.
Yürekli, K., Kurunç, A., and Öztürk, F., Testing the residuals of an ARIMA model on the Cekerek Stream Watershed in Turkey, Turkish J. Engin. Environ. Sciences, 2005, vol. 29, no. 2, pp. 61–74.
Zakermoshfegh, M., Ghodsian, M., and Montazer, G.A., River flow forecasting using artificial neural networks, Hydraulics of Dam and River Structures, 2004, vol. 90, pp. 425–430.
Author information
Authors and Affiliations
Corresponding author
Additional information
The article is published in the original.
Rights and permissions
About this article
Cite this article
Elganiny, M.A., Eldwer, A.E. Enhancing the Forecasting of Monthly Streamflow in the Main Key Stations of the River Nile Basin. Water Resour 45, 660–671 (2018). https://doi.org/10.1134/S0097807818050135
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0097807818050135