Forecasting daily river flow using an artificial flora–support vector machine hybrid modeling approach (case study: Karkheh Catchment, Iran)

Document Type : Research Paper

Authors

1 PhD student in aquatic structures, Lorestan University, Lorestan, Iran

2 Associate Professor, Department of Water Engineering, Lorestan University, Lorestan, Iran

3 Assistant Professor, Department of Water Engineering, Lorestan University, Lorestan, Iran

Abstract

In this study, a hybrid support vector machine–artificial flora algorithm method was developed to estimate the flow rate of Karkheh Catchment rivers using daily discharge statistics. The results were compared with those of the support vector–wave vector machine model. The daily discharge statistics were taken from hydrometric stations located upstream of the dam in the statistical period 2008 to 2018. Necessary criteria including coefficient of determination, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe coefficient were used to evaluate and compare the models. The results illustrated that the combined structures provided acceptable results in terms of river flow modeling. Also, a comparison of the models based on the evaluation criteria and Taylor’s diagram demonstrated that the proposed hybrid method with the correlation coefficient R2= 0.924-0.974, root-mean-square error RMSE= 0.022-0.066 m3/s, mean absolute error MAE= 0.011-0.034 m3/s, and Nash-Sutcliffe coefficient NS=0.947-0.986 outperformed other methods in terms of estimating the daily flow rates of the rivers.
 

Keywords

References

Adnan,  R., Liang,  Z., Heddam, S., Kermani, M., Kisi, O. and Li, B. 2019.  Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology. 19(4), 432-448
Alizadeh,  F., Gharamaleki, A., Jalilzadeh, M. and Akhoundzadeh, A. 2020.  Prediction of river stage-discharge process based on a conceptual model using EEMD-WT-LSSVM Approach,  Water Resources. 47, 41-53
Basak,  D., Pal,  S. and Patranabis,  D.C. 2007. Support vector regression.  Neural Inf Process. 11, 203-225.
Cartlidge,  J.P. and Bulloc,  S.G. 2004.  Combating coevolutionary disengagement by reducing parasite virulence. Evolutionary Computation.12(2),193-222
Chen, H. and Zhu, Y. 2008. Optimization based on symbiotic multi-species coevolution. Journal on applied mathematics and computation, 22(3), 179-194
Cheng,  L., Wu, X. and Wang, Y. 2018.  Artificial Flora (AF) Optimization Algorithm. Applied science. 329(8), 2-22.
Edossa, D.C. and Babel, M.S. 2012. Forecasting Hydrological DroughtsUsing  Artificial  Neural Network  Modeling  Technique  SouthAfrica: University of Pretoria Proceedings of 16th SANCIAHSNational  Hydrology  Symposium, 1–10.
Ghorbani, M.A.,  Khatibi, R., Geol, A., Fazelifard, M.H. and Azani, A. 2016. Modeling river discharge time series using support vector machine and artificial neural networks. Environmental Earth Sciences. 75(4), 675-685
Hamel, L. 2009. Knowledge discovery with support vector Machines, hoboken, N.J. John Wiley.
Hillis, W.D. 1990. Co-evolving Parasites Improve Simulated Evolution as an Optimization Procedure. Phys  D Nonlinear Phenom. 42, 228–234.
Huang, S., Chang, J., Huang, Q. and Chen, Y. 2014. Monthly streamflow prediction using modified emd-based support vector machine. Journal of Hydrology. 511(4), 764-775.
Hussain, D. and Ahmed Khan, A. 2020. Machine learning techniques for monthly river flow forecasting of Hunza River, Pakistan. Earth Science Informatics. 14(5),1824-1836.
Kalteh, A.M. 2013. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Computers & Geosciences. 54, 1-8.
Kisi, O., Karahan, M. and Sen, Z. 2006. River suspended sediment modeling using fuzzy logic approach. Hydrol Process. 20(2), 4351-4362.
Lin, J.Y., Cheng, C.T. and Chau, K.W. 2006. Using support vector machines for long-term discharge prediction.  Hydrolog Sciences Journal. 51(3), 599-612.
Liong, S.Y., and Sivapragasam, C. 2002. Flood stage forecasting with support vector machines.  JAWRA Journal of the American Water Resources Association. 38(4), 173–186.
Misra, D., Oommen, T., Agarwa, A., Mishra, S.K., and Thompson, A.M. 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield.  Results.Biosystems Engineering. 103(3), 527–535
Nagy,  H., Watanabe, K. and Hirano, M. 2002. Prediction of sediment load concentration in rivers using artificial neural network model.  Journal of Hydraulics Engineering. 128(3), 558-559.
Othman, F. and Naseri, M. 2011. Reservoir inflow forecasting using artificial neural network International .Journal of the Physical Sciences. 6(3), 434-440.
Pagie, L. and Mitchell, M.A. 2002. Comparison of evolutionary and coevolutionary search. International .Journal of Computational Intelligence and Application. 2, 53-69.
Rajaee, T., Khani, S. and Ravansalar, M. 2020 Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory System. 8(5), 1324-1336.
Rosin,  C.D. and Belew, R.K. 1995. Methods for Competitive Co-Evolution: Finding Opponents Worth .Beating  In Proceedings of the International Conference on Genetic Algorithms  Pittsburgh. 373-381.
Samadianfard, S., Jarhan, S., Salwana, E., Mosavi, A., Shamshirband, S. and Akib, S. 2019. Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin. Water. 11(9),1934-1945
Sedighi, F., Vafakhah, M. and Javadi, M.R. 2016.  Rainfall–Runoff modeling using support vector machine in snow-affected watershed .Arabian Journal for Science and Engineering. 41(10), 4065-4076
Shin,  S.,  Kyung,  D.,  Lee,  S., Taik Kim,  J. and Hyun, J. 2005.  An application of support vector machines in bankruptcy prediction model, Expert Systems with Applications. 28(4), 127-135.
Taylor, E. 2001. Summarizing multiple aspects of model performance in a single diagram Journal of Geophysical Research. 106(7), 7183-7192.
Vapnik, V.N. 1995.  The Nature of Statistical Learning Theory. Springer, New York
Vapnik, V.N. 1998. Statistical learning theory. Wiley, New York
Vapnik, V. and Chervonenkis, A. 1991. The necessary and sufficient conditions for consistency in the empirical risk minimization method  Pattern. Recognition and Image Analysis. 1(3), 283-305.
Wang, D., Safavi, A.A and Romagnoli, J.A. 2000. Wavelet-based adaptive robust M-estimator for non-linear system identification.  AIChE Journal. 46(4),1607-1615.
Wiegand, R.P. and Sarma, J. 2004. Spatial Embedding and Loss of Gradient in Cooperative Coevolutionary Algorithms,  In Proceedings of the International Conference on Parallel Problem Solving from Nature, Berlin  Germany. 43, 912–921.
Williams, N. and Mitchell, M. 2005. Investigating the success of spatial coevolution. In Proceedings of the 7th Annual Conference on Genetic And Evolutionary. Computation Washington. 46, 523-530.
Yoon, H., Jun, S.C., Hyun, Y., Bae, G.O. and Lee, K.K. 2011. A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer .Journal of  Hydrology. 396(4), 128-138
Zhang, G., Patuwo, B.E., and Hu, Y.M. 1998. Forecasting with artificialneural networks: The state of the art .International Journal of Forecasting. 14(1), 35-62.
Zhu, Y.M., Lu,  X.X. and  Zhou, Y. 2007. Suspended sediment flux modeling with artificial neural network: An example of the longchuanjiang river in the upper yangtze catchment.  Geomorphology. 84(4), 111-125.
Environmental Resources Research
Volume 8, Issue 2
July 2020
Pages 161-174

Files

Share

How to cite

Statistics