Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale

Affiliation(s)

Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.

Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.

ABSTRACT

In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.

In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.

Cite this paper

P. Bhagwat and R. Maity, "Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale,"*Journal of Water Resource and Protection*, Vol. 4 No. 7, 2012, pp. 528-539. doi: 10.4236/jwarp.2012.47062.

P. Bhagwat and R. Maity, "Multistep-ahead River Flow Prediction using LS-SVR at Daily Scale,"

References

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[2] J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, Vol. 9, No. 3, 1999, pp. 293-300. doi:10.1023/A:1018628609742

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[14] I. M. Khadam and J. J. Kaluarachchi, “Use of Soft Information to Describe the Relative Uncertainty of Calibration Data in Hydrologic Models,” Water Resources Research, Vol. 40, No. W11505, 2004, p. 15. doi: 10.1029/2003WR002939

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[16] Z. Qin, Q. Yu, J. Li, Z. Wu and B. Hu, “Application of Least Squares Vector Machines in Modelling Water Vapor and Carbon Dioxide Fluxes Over a Cropland,” Journal of Zhejiang University Science, Vol. B6, No. 6, 2005, pp. 491-495, doi: 10.1631/jzus.2005.B0491

[17] P. Aksornsingchai and C. Srinilta, “Statistical Downscaling for Rainfall and Temperature Prediction in Thailand,” Proceeding of the International MultiConference of Engineers and Computer Scientists, Hong Kong, Vol. 1, 16-18 March 2011.

[18] G. Zhang, B. E. Patuwo and M. Y. Hu, “Forecasting with Artificial Neural Networks: The State of the Art,” International Journal of Forecasting, Vol. 14, No. 1, 1998, pp. 35-62. doi:10.1016/S0169-2070(97)00044-7

[19] T. Naes, K. Kvaal, T. Isaksson and C. Miller, “Artificial Neural Networks in Multivariate Calibration,” Journal of Near-Infrared Spectroscopy, Vol. 1, 1993, pp. 1-11. doi:10.1255/jnirs.1

[20] A. Y. Shamseldin, “Application of a Neural Network Technique to Rainfall-Runoff Modeling,” Journal of Hydrology, Vol. 199, No. 3, 1997, pp. 272-294.

[21] C. M. Zealand, D. H. Burn and S. P. Simonovic, “Short Term Stream Flow Forecasting Using Artificial Neural Networks,” Journal of Hydrology, Vol. 214, No. 1-4, 1999, pp. 32-48. doi:10.1016/S0022-1694(98)00242-X

[22] A. S. Weigend, D. E. Rumelhart and B. A. Huberman, “Predicting the Future: A Connectionist Approach,” International Journal of Neural Systems, Vol. 1, No. 3, 1992, pp. 193-209.

[23] R. Maity and D. Nagesh Kumar, “Basin-Scale Stream Flow Forecasting Using the Information of Large-Scale Atmospheric Circulation Phenomena,” Hydrological Processes, Vol. 22, No. 5, 2008, pp. 643-650. doi:10.1002/hyp.6630

[24] P. Coulibaly and N. D. Evora, “Comparison of Neural Network Methods for Infilling Missing Daily Weather Records,” Journal of Hydroogy, Vol. 341, No. 1-2, 2007, pp. 27-41. doi:10.1016/j.jhydrol.2007.04.020

[25] H. F. Zou, G. P. Xia, F. T. Yang and H. Y. Wang, “An Investigation and Comparison of Artificial Neural Network and Time Series Models for Chinese Food Grain Price Forecasting,” Neurocomputing, Vol. 70, No. 16-18, 2007, pp. 2913-2923. doi:10.1016/j.neucom.2007.01.009

[26] ASCE, “Artificial neural networks in hydrology. II: Hydrologic applications,” ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)

[27] T. Van Gestel, J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor and J. Vandewalle, “Benchmarking Least Squares Support Vector Machine Classifiers,” Machine Learning, Vol. 54, No. 1, 2004, pp. 5-32. doi:10.1023/B:MACH.0000008082.80494.e0

[28] W.H. Chen, J.Y. Shih and S. Wu, “Comparison of Support-Vector Machines and Back Propagation Neural Networks in Forecasting the Six Major Asian Stock Markets,” International Journal of Electronic Finance, Vol. 1, No. 1, 2006 , pp. 49-67.

[29] R. M. Balabin and E. I. Lomakina, “Support Vector Machine Regression (LS-SVM)—An Alternative to Artificial Neural Networks (ANNs) for the Analysis of Quantum Chemistry Data?” Physical Chemistry Chemical Physics, Vol. 13, No. 24, 2011, pp. 11710-11718. doi:10.1039/c1cp00051a

[1] V. N. Vapnik, “Statistical Learning Theory,” John Wiley and Sons, New York, 1998.

[2] J. A. K. Suykens and J. Vandewalle, “Least Squares Support Vector Machine Classifiers,” Neural Processing Letters, Vol. 9, No. 3, 1999, pp. 293-300. doi:10.1023/A:1018628609742

[3] B. E. Boser, I. Guyon and V. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” Proceedings Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, 1992, pp. 144-152.

[4] H. D. Drucker, C. J. C. Burges, L. Kaufman, A. Smola and V. Vapnik, “Support Vector Regression Machines,” In: M. C. Mozer, M. I. Jordan and T. Petsche, Eds., Advances in Neural Information Processing Systems, Vol. 9, Morgan Kaufmann, San Mateo, 1997, pp. 155-161.

[5] Y. B. Dibike, S. Velickov, D. Slomatine and M. B. Abbott, “Model Induction with Support Vector Machines: Introduction and Applications,” Journal of Computing in Civil Engineering, Vol. 15, No. 3, 2001, pp. 208-216. doi: 10.1061/(ASCE)0887-3801(2001)15:3(208)

[6] S. Tripathi, V. V. Srinivas and R. S. Nanjundian, “Downscaling of Precipitation for Climate Change Scenarios: A Support Vector Machine Approach,” Journal of Hydrology, Vol. 330 No. 3-4, 2006, pp. 621-640. doi: 10.1016/j.jhydrol.2006.04.030

[7] W. Wu, X. Wang, D. Xie and H. Liu, “Soil Water Content Forecasting by Support Vector Machine in Purple Hilly Region,” International Federation for Information Processing, Vol. 258, 2008, pp. 223-230. doi:10.1007/978-0-387-77251-6_25

[8] R. Maity, P. P. Bhagwat and A. Bhatnagar, “Potential of Support Vector Regression for Prediction of Monthly Streamflow Using Endogenous Property,” Hydrological Processes, Vol. 24, No. 7, 2010, pp. 917-923. doi: 10.1002/hyp.7535

[9] S.-Y. Liong and C. Sivapragasam, “Flood Stage Forecasting with Support Vector Machines,” Journal of the American Water Resources Association, Vol. 38, No. 1, 2002, pp. 173-196. doi:10.1111/j.1752-1688.2002.tb01544.x

[10] M. Bray and D. Han, “Identification of Support Vector Machines for Runoff Modelling,” Journal of Hydroinformatics, Vol. 6, No. 4, 2004, pp. 265-280.

[11] P. Samui, “Application of Least Square Support Vector Machine (LSSVM) for Determination of Evaporation Losses in Reservoirs,” Engineering, Vol. 3, No. 4, 2011, pp. 431-434. doi:10.4236/eng.2011.34049

[12] N. She and D. Basketfield, “Long Range Forecast of Stream Flow Using Support Vector Machine,” Proceedings of the World Water and Environment Resources Congress, ASCE, Anchorage, 2005. doi:10.1061/40792(173)481

[13] X. Zhang, R. Srinivasan and M. V. Liew, “Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine,” Journal of the American Water Resources Association, Vol. 45, No. 2, 2009, pp. 460-474. doi:10.1111/j.1752-1688.2009.00302.x

[14] I. M. Khadam and J. J. Kaluarachchi, “Use of Soft Information to Describe the Relative Uncertainty of Calibration Data in Hydrologic Models,” Water Resources Research, Vol. 40, No. W11505, 2004, p. 15. doi: 10.1029/2003WR002939

[15] J. A.K. Suykens, J. De Brabanter, L. Lukas and J. Vandewalle, “Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation,” Neurocomputing, Vol. 48, No. 1-4, 2002, pp. 85-105. doi:10.1016/S0925-2312(01)00644-0

[16] Z. Qin, Q. Yu, J. Li, Z. Wu and B. Hu, “Application of Least Squares Vector Machines in Modelling Water Vapor and Carbon Dioxide Fluxes Over a Cropland,” Journal of Zhejiang University Science, Vol. B6, No. 6, 2005, pp. 491-495, doi: 10.1631/jzus.2005.B0491

[17] P. Aksornsingchai and C. Srinilta, “Statistical Downscaling for Rainfall and Temperature Prediction in Thailand,” Proceeding of the International MultiConference of Engineers and Computer Scientists, Hong Kong, Vol. 1, 16-18 March 2011.

[18] G. Zhang, B. E. Patuwo and M. Y. Hu, “Forecasting with Artificial Neural Networks: The State of the Art,” International Journal of Forecasting, Vol. 14, No. 1, 1998, pp. 35-62. doi:10.1016/S0169-2070(97)00044-7

[19] T. Naes, K. Kvaal, T. Isaksson and C. Miller, “Artificial Neural Networks in Multivariate Calibration,” Journal of Near-Infrared Spectroscopy, Vol. 1, 1993, pp. 1-11. doi:10.1255/jnirs.1

[20] A. Y. Shamseldin, “Application of a Neural Network Technique to Rainfall-Runoff Modeling,” Journal of Hydrology, Vol. 199, No. 3, 1997, pp. 272-294.

[21] C. M. Zealand, D. H. Burn and S. P. Simonovic, “Short Term Stream Flow Forecasting Using Artificial Neural Networks,” Journal of Hydrology, Vol. 214, No. 1-4, 1999, pp. 32-48. doi:10.1016/S0022-1694(98)00242-X

[22] A. S. Weigend, D. E. Rumelhart and B. A. Huberman, “Predicting the Future: A Connectionist Approach,” International Journal of Neural Systems, Vol. 1, No. 3, 1992, pp. 193-209.

[23] R. Maity and D. Nagesh Kumar, “Basin-Scale Stream Flow Forecasting Using the Information of Large-Scale Atmospheric Circulation Phenomena,” Hydrological Processes, Vol. 22, No. 5, 2008, pp. 643-650. doi:10.1002/hyp.6630

[24] P. Coulibaly and N. D. Evora, “Comparison of Neural Network Methods for Infilling Missing Daily Weather Records,” Journal of Hydroogy, Vol. 341, No. 1-2, 2007, pp. 27-41. doi:10.1016/j.jhydrol.2007.04.020

[25] H. F. Zou, G. P. Xia, F. T. Yang and H. Y. Wang, “An Investigation and Comparison of Artificial Neural Network and Time Series Models for Chinese Food Grain Price Forecasting,” Neurocomputing, Vol. 70, No. 16-18, 2007, pp. 2913-2923. doi:10.1016/j.neucom.2007.01.009

[26] ASCE, “Artificial neural networks in hydrology. II: Hydrologic applications,” ASCE Task Committee on Application of Artificial Neural Networks in Hydrology, Journal of Hydrologic Engineering, Vol. 5, No. 2, 2000, pp. 124-137. doi:10.1061/(ASCE)1084-0699(2000)5:2(124)

[27] T. Van Gestel, J. A. K. Suykens, B. Baesens, S. Viaene, J. Vanthienen, G. Dedene, B. De Moor and J. Vandewalle, “Benchmarking Least Squares Support Vector Machine Classifiers,” Machine Learning, Vol. 54, No. 1, 2004, pp. 5-32. doi:10.1023/B:MACH.0000008082.80494.e0

[28] W.H. Chen, J.Y. Shih and S. Wu, “Comparison of Support-Vector Machines and Back Propagation Neural Networks in Forecasting the Six Major Asian Stock Markets,” International Journal of Electronic Finance, Vol. 1, No. 1, 2006 , pp. 49-67.

[29] R. M. Balabin and E. I. Lomakina, “Support Vector Machine Regression (LS-SVM)—An Alternative to Artificial Neural Networks (ANNs) for the Analysis of Quantum Chemistry Data?” Physical Chemistry Chemical Physics, Vol. 13, No. 24, 2011, pp. 11710-11718. doi:10.1039/c1cp00051a