Sains Malaysiana 43(12)(2014): 1865–1871

Development of Generalized Feed Forward Network for Predicting Annual Flood (Depth)

of a Tropical River

(Pembangunan Rangkaian Suapan ke Hadapan Menyeluruh untuk Meramalkan Banjir Tahunan (Kedalaman) Sungai Tropika)

MOHSEN SALARPOUR¹, ZULKIFLI YUSOP²*, MILAD JAJARMIZADEH¹ & FADHILAH YUSOF³

¹Faculty of Civil Engineering, Department of Hydraulic and Hydrology

Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

²Water Research Alliance, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

³Faculty of Science, Department of Mathematics, Universiti Teknologi Malaysia

81310 Skudai, Johor, Malaysia

Received: 17 August 2013/Accepted: 16 April 2014

ABSTRACT

The modeling of rainfall-runoff relationship in a watershed is very important in designing hydraulic structures, controlling flood and managing storm water. Artificial Neural Networks (ANNs) are known as having the ability to model nonlinear mechanisms. This study aimed at developing a Generalized Feed Forward (GFF) network model for predicting annual flood (depth) of Johor River in Peninsular Malaysia. In order to avoid over training, cross-validation technique was performed for optimizing the model. In addition, predictive uncertainty index was used to protect of over parameterization. The governing training algorithm was back propagation with momentum term and tangent hyperbolic types was used as transfer function for hidden and output layers. The results showed that the optimum architecture was derived by linear tangent hyperbolic transfer function for both hidden and output layers. The values of Nash and Sutcliffe (NS) and root mean square error (RMSE) obtained 0.98 and 5.92 for the test period. Cross validation evaluation showed 9 process elements is adequate in hidden layer for optimum generalization by considering the predictive uncertainty index obtained (0.14) for test period which is acceptable.

Keywords: Annual flood; artificial neural networks; cross validation; generalized feed forward; Johor River; predictive uncertainty

ABSTRAK

Pemodelan hubungan curahan hujan-aliran air di suatu kawasan tadahan adalah sangat penting dalam mereka bentuk struktur hidraulik, mengawal banjir dan menguruskan air ribut. Rangkaian neural tiruan (ANNs) dikenal pasti mempunyai keupayaan untuk memperaga mekanisme tak linear. Kajian ini bertujuan untuk membangunkan model rangkaian suapan ke hadapan menyeluruh (GFF) untuk meramalkan banjir tahunan (kedalaman) Sungai Johor di Semenanjung Malaysia. Untuk mengelakkan latihan berlebihan, teknik pengesahan silang telah dijalankan bagi mengoptimumkan model tersebut. Di samping itu, indeks ketidakpastian ramalan digunakan untuk melindungi daripada pemparameteran berlebihan. Algoritma latihan pentadbiran adalah perambatan balik terma momentum dan jenis tangen hiperbolik digunakan sebagai fungsi perpindahan bagi lapisan tersembunyi dan output. Hasil kajian menunjukkan bahawa seni bina yang optimum diperoleh melalui fungsi perpindahan linear tangen hiperbolik bagi lapisan tersembunyi dan output. Nilai Nash dan Sutcliffe (NS) serta punca min ralat kuasa dua (RMSE) memperoleh 0.98 dan 5.92 bagi masa ujian. Penilaian pengesahan silang menunjukkan 9 proses elemen adalah mencukupi dalam lapisan tersembunyi untuk pengitlakan yang optimum dengan mengambil kira ramalan indeks ketidaktentuan yang diperoleh (0.14) dalam masa ujian adalah diterima.

Kata kunci: Banjir tahunan; ketidakpastian ramalan; pengesahan silang; rangkaian neural tiruan; suapan ke hadapan menyeluruh; Sungai Johor

REFERENCES

Altunkaynak, A. 2007. Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resources Management 21(2): 399-408.

ASCE. 2000a. Artificial neural networks in hydrology. I: preliminary concepts. Journal of Hydrologic Engineering 5(2): 115-123.

ASCE. 2000b. Artificial neural networks in hydrology. I: hydrologic applications. Journal of Hydrologic Engineering 5(2): 124-137.

Agarwal, A., Mishra, S.K., Ram, S. & Singh, J.K. 2006. Simulation of runoff and sediment yield using artificial neural networks. Biosystems Engineering 94(4): 597-613.

Bishop, C.M. 1995. Neural Networks for Pattern Recognition. Oxford: Clarendon Press.

Bowden, G.J., Dandy, G.C. & Maier, H.R. 2005. Input determination for neural network models in water resources applications. Part 1-background and methodology. Journal of Hydrology 301(1): 75-92.

Can, I. 2002. A new improved Na/K geothermometer by artificial neural networks. Geothermics 31(6): 751-760.

Dawson, C.W. & Wilby, R. 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal 43(1): 47-66.

De Vos, N.J. & Rientjes, T.H.M. 2005. Constraints of artificial neural networks for rainfall-runoff modelling: Trade-offs in hydrological state representation and model evaluation. Hydrology and Earth System Sciences Discussions 2(1): 365-415.

Elshorbagy, A., Simonovic, S.P. & Panu, U.S. 2000. Performance evaluation of artificial neural networks for runoff prediction. Journal of Hydrologic Engineering 5(4): 424-427.

El-Shafie, A. & Noureldin, A. 2010. Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam. Hydrology and Earth System Sciences Discussions 7(5): 7957-7993.

El-Shafie, A., Noureldin, A., Taha, M.R. & Hussain, A. 2011. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrology and Earth System Sciences Discussions 8(4): 6489-6532.

Ghumman, A.R., Ghazaw, Y.M., Sohail, A.R. & Watanabe, K. 2011. Runoff forecasting by artificial neural network and conventional model. Alexandria Engineering Journal 50(4): 345-350.

Jajarmizadeh, M., Harun, S., Abdullah, R. & Salarpour, M. 2012a. Using soil and water assessment tool for flow simulation and assessment of sensitive parameters applying SUFI-2 algorithm. Caspian Journal of Applied Sciences Research 2(1): 37-47.

Jajarmizadeh, M., Harun, S. & Salarpour, M. 2012b. A review on theoretical consideration and types of models in hydrology. Journal of Environmental Science and Technology 5(5): 249-261.

Jain, A.K., Mao, J. & Mohiuddin, K.M. 1996. Artificial neural network: A tutorial. IEEE - Computer 29(3): 31-44.

Jamaludin Suhaila, Sayang Mohd Deni, Wan Zawiah Wan Zin & Abdul Aziz Jemain 2010. Trends in Peninsular Malaysia rainfall data during the Southwest Monsoon and Northeast Monsoon seasons: 1975-2004. Sains Malaysiana 39(4): 533-542.

Wu, J.S., Han, J., Annambhotla, S. & Bryant, S. 2005. Artificial neural networks for forecasting watershed runoff and stream flows. Journal of Hydrologic Engineering 10(3): 216-222.

Parasuraman, K., Elshorbagy, A. & Carey, S.K. 2006. Spiking modular neural networks: A neural network modeling approach for hydrological processes. Water Resources Research 42(5). DOI: 10.1029/2005WR004317.

Kişi, Ö. 2009. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering 14(8): 773-782.

Kişi, Ö. 2008. River flow forecasting and estimation using different artificial neural network techniques. Hydrological Resources 39(1): 27-40.

Kişi, Ö. 2007. Stream flow forecasting using different artificial neural network algorithms. Journal of Hydrologic Engineering 12(5): 532-539.

Kişi, Ö. 2006. Evapotranspiration estimation using feed-forward neural networks. Nordic Hydrology 37(3): 247-260.

Kişi, Ö., Moghaddam Nia, A.R., Ghafari Gosheh, M., Jamalizadeh Tajabadi, M.R. & Ahmadi, A. 2012. Intermittent stream flow forecasting by using several data driven techniques. Water Resources Management 26(2): 457-474.

Maier, H.R. & Dandy, G.C. 2000. Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environ. Modeling and Software 15(1): 101-124.

Menhaj, M.B. 2010. Computational Intelligence, Fundamentals of Neural Networks. Iran: Amir Kabir University Publication.

Mutlu, E., Chaubey, I., Hexmoor, H. & Bajwa, S.G. 2008. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrological Process 22(26): 5097-5106.

Nayebi, M., Khalili, D., Amin, S. & Zand-Parsa, S.H. 2006. Daily stream flow prediction capability of artificial neural networks as influenced by minimum air temperature data. Bio System Engineering 95(4): 557-567.

Nourani, V. & Kalantari, O. 2010. Integrated artificial neural network for spatiotemporal modeling of rainfall-runoff-sediment processes. Environmental Engineering Science 27(5): 411-422.

Rajurkar, M.P., Kothyari, U.C. & Chaube, U.C. 2004. Modeling of daily rainfall-runoff relationship with artificial neural network. Journal of Hydrology 285(1): 96-113.

Rezaeian Zadeh, M., Amin, S., Khalili, D. & Singh, P.V. 2010. Daily outflow prediction by multi-layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resources Management 24(11): 2673-2688.

Shamsudin, R., Saad, P. & Shabri, A. 2011. River flow time series using least squares support vector machines. Hydrology and Earth System Science 15(6): 1835-1852.

Senthil Kumar, A.R., Sudheer, K.P., Jain, S.K. & Agarwal, P.K. 2005. Rainfall-runoff modeling using artificial neural networks: Comparison of network types. Hydrological Process 19(6): 1277-1291.

Singh, A., Imtiyaz, M., Isaac, R.K. & Denis, D.M. 2012. Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India. Agricultural Water Management 104: 113-120.

Sorayya, M., Aishah, S. & Mohd, B. 2012. Supervised and unsupervised artificial neural networks for analysis of diatom abundance in Tropical Putrajaya Lake, Malaysia. Sains Malaysiana 41(8): 939-947.

Srivastava, P., McNair, J.N. & Johnson, T.E. 2006. Comparison of process-based and artificial neural network approaches for stream flow modeling in an agricultural watershed. Journal of the American Water Resources Association 42(3): 545-563.

Tokar, A.S. & Johnson, P.A. 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering 4(3): 232-239.

Tombul, M. & Ersin, O. 2006. Modeling of rainfall-runoff relationship at the semi-arid small catchments using artificial neural networks. Lecture Notes in Control and Information Sciences 344: 309-318.

World Meteorological Organization. 1975. Inter-comparison of conceptual models used in operational hydrological forecasting. World meteorological Organization. Techinical report 429, Geneva, Switzerland.

Wahidah Sanusi. & Kamarulzaman Ibrahim. 2012. Application of loglinear models in stimating wet category in monthly rainfall. Sains Malaysiana 41(11): 1345-1353.

Xu, Q., Ren, L., Yu, Z., Yang, B. & Wang, G. 2008. Rainfall-runoff modeling at daily scale with artificial neural networks, Fourth International Conference on Natural Computation Oct 18-20. Jinan.

*Corresponding author; email: zulyusop@utm.my