Modeling river stage-discharge-sediment rating relation using support vector regression

Hydrology Research

Volumn 43, Issue 6, 2012, Pages 851-861

  Jain, Sharad K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

Artificial neural network; River stage discharge rating; Sediment concentration; Support vector regression

Indexed keywords

DATA-DRIVEN APPROACH; GENERALIZATION ERROR; HYDROLOGICAL DATA; KERNEL-BASED LEARNING; LEARNING APPROACH; MODELING TECHNIQUE; REGRESSION PROBLEM; RIVER DISCHARGE; RIVER STAGES; SEDIMENT CONCENTRATION; STAGE-DISCHARGE; STRUCTURAL RISK MINIMIZATION PRINCIPLE; SUPPORT VECTOR REGRESSION (SVR);

DIGITAL STORAGE; FUZZY LOGIC; NEURAL NETWORKS; RATING; RIVERS; SEDIMENTATION; STREAM FLOW; SUPPORT VECTOR MACHINES;

REGRESSION ANALYSIS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CONCENTRATION (COMPOSITION); ERROR ANALYSIS; FUZZY MATHEMATICS; HYDROLOGICAL MODELING; REGRESSION ANALYSIS; RIVER DISCHARGE; SEDIMENT TRANSPORT;

EID: 84871381454     PISSN: 00291277     EISSN: None     Source Type: Journal    
DOI: 10.2166/nh.2011.101     Document Type: Article

References (55)

1. Abrahart, R. J. & See, L. M. 2007 Neural network emulation of a rainfall-runoff model. Hydrol. Earth Syst. Sci. Discuss. 4, 287-326. (Pubitemid 46889139)
2. Anandhi, A., Srinivas, V. V., Nanjundiah, R. S. & Nagesh Kumar, D. 2008 Downscaling precipitation to river basin in India for IPCC SRES scenarios using support vector machine. Intl. J. Climatol. 28, 401-420. (Pubitemid 351411413)
3. ASCE 2000 Task Committee on application of artificial neural networks in hydrology. artificial neural networks in hydrology, I: preliminary concepts.J. Hydrol. Eng. ASCE5, 115-123.
4. Behzad, M., Asghari, K., Eazi, M. & Palhang, M. 2009 Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst. Appl. 36, 7624-7629.
5. Bray, M. & Han, D. 2004 Identification of support vector machines for runoff modeling. J. Hydroinformatics 6, 265-280.
6. Burges, C. J. C. 1998 A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121-167. (Pubitemid 128695475)
7. Campolo, M., Soldati, A. & Andreussi, P. 2003 Artificial neural network approach to flood forecasting in the River Arno. Hydrol. Sci. J. 48, 381-398. (Pubitemid 36695915)
8. Chang, F. J. & Chen, Y. C. 2001A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. J. Hydrol. 245, 153-164.
9. Cigizoglu, H. K. 2003 Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrol. Sci. J. 48, 349-361.
10. Cigizoglu, H. K. 2004 Estimation and forecasting of daily suspended sediment data by multi layer perceptrons. Adv. Water Resour. 27, 185-195. (Pubitemid 38256721)
11. Cigizoglu, H. K. & Kisi, O. 2005 Flow prediction by three back propagation techniques using K-fold partitioning of neural network training data. Nordic Hydrol. 36, 49-64. (Pubitemid 40620979)
12. Çimen, M. 2008 Estimation of daily suspended sediments using support vector machines. Hydrol. Sci. J. 53, 656-666. (Pubitemid 351850446)
13. Çimen, M. & Kisi, O. 2009 Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J. Hydrol. 378, 253-262.
14. Coulibaly, P., Anctil, F. & Bobee, B. 2001 Multivariate reservoir inflow forecasting using temporal neural networks. J. Hydrol. Eng. ASCE 6, 367-376. (Pubitemid 32818650)
15. Dibike, Y. B., Velickov, S., Solomatine, D. P. & Abbott, M. B. 2001 Model induction with support vector machines: introduction and applications. J. Comput. Civil Eng. ASCE 15, 208-216. (Pubitemid 32583199)
16. Gunn, S. R. 1998 Support vector machines for classification and regression. Technical Report, University of Southampton, UK.
17. Han, D., Chan, L. & Zhu, N. 2007 Flood forecasting using support vector machines. J. Hydroinformatics 9, 267-276.
18. Hsu, C. W., Chang, C. C. & Lin, C. J. 2003 A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University, Taiwan.
19. Hu, T. S., Lam, K. C. & Ng, S. T. 2005 A modified neural network for improving river flow prediction. Hydrol. Sci. J. 50, 299-318.
20. Imrie, C. E., Durucan, S. & Korre, A. 2000 River flow prediction using artificial neural networks: generalization beyond the calibration range. J. Hydrol. 233, 138-153. (Pubitemid 30388023)
21. Jain, S. K. 2001 Development of integrated sediment rating curves using ANNs. J. Hydraul. Eng. ASCE 127, 30-37.
22. Jain, S. K., Das, D. & Srivastava, D. K. 1999 Application of ANN for reservoir inflow prediction and operation. J. Water Resour. Plan. Manage. ASCE 125, 263-271.
23. Jain, S. K., Singh, V. P. & van Genuchten, M. Th. 2004 Analysis of soil water retention data using Artificial Neural Networks. J. Hydrol. Eng. ASCE 9, 415-420. (Pubitemid 39169136)
24. Khadam, I. M. & Kaluarachchi, J. J. 2004 Use of soft information to describe the relative uncertainty of calibration data in hydrologic models. Water Resour. Res. 40, W11505.
25. Khan, M. S. & Coulibaly, P. 2006 Application of support vector machine in lake water level prediction. J. Hydrol. Eng. 11, 199-205.
26. Kisi, O. 2005 Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol. Sci. J. 50, 683-696. (Pubitemid 41057429)
27. Kisi, O. 2007 Streamflow forecasting using different artificial neural network algorithms. J. Hydrol. Eng. ASCE 12, 532-539. (Pubitemid 47307903)
28. Kisi, O. & Çimen, M. 2009 Evapotranspiration modelling using support vector machines. Hydrol. Sci. J. 54, 918-928.
29. Lamorski, K., Pachepsky, Y., Slawinski, C. & Walczak, R. T. 2008 Using support vector machines to develop pedotransfer functions for water retention of soils in Poland. Soil Sci. Soc. Am. J. 72, 1243-1247.
30. Lekkas, D. F., Imrie, C. E. & Lees, M. J. 2001 Improved non-linear transfer function and neural network methods of flow routing for real-Time forecasting. J. Hydroinformatics 3, 153-164.
31. Li, P.-H., Kwon, H.-H., Sun, L., Lall, U. & Kaoa, J.-J. 2010 A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, Taiwan. Intl. J. Climatol. 30, 1256-1268.
32. Lin, J.-Y., Cheng, C.-T. & Chau, K.-W. 2006 Using support vector machines for long-Term discharge prediction. Hydrol. Sci. J. 51, 599-612. (Pubitemid 44178040)
33. Liong, S. & Sivapragasam, C. 2002 Flood stage forecasting with support vector machines. J. Am. Water Resour. Assoc. 38, 173-186. (Pubitemid 34253505)
34. Maity, R., Bhagwat, P. P. & Bhatnagar, A. 2010 Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol. Process. 24, 917-923.
35. Nayak, P. C., Sudheer, K. P., Rangan, D. M. & Ramasastri, K. S. 2004 Neuro-fuzzy computing technique for modelling hydrological time series. J. Hydrol. 291, 52-66. (Pubitemid 38492436)
36. Osuna, E., Freund, R. & Girosi, F. 1997 An improved training algorithm for support vector machines. In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing 7, Institute of Electrical and Electronics Engineers, New York, pp. 276-285.
37. Prada-Sarmiento, F. & Obregón-Neira, N. 2009 Forecasting of monthly streamflows based on artificial neural networks. J. Hydrol. Eng. ASCE 14, 1390-1395.
38. Partal, T. & Cigizoglu, H. K. 2009 Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J. Hydrol. 358, 317-331.
39. Qin, Z., Qiang, Y U., Jun, L., Zhi-yi, W. & Bing-min, H. 2005 Application of least squares vector machines in modelling water vapor and carbon dioxide fluxes over a cropland. J. Zhejiang Univ. SCI 6B (6), 491-495. (Pubitemid 40915667)
40. She, N. & Basketfield, D. 2005 Long range forecast of streamflow using support vector machine. In: Proceedings of the World Water and Environment Resources Congress ASCE (R. Walton, ed.). May 15-19, 2005, Anchorage, Alaska, USA.
41. Singh, K. K., Pal, M., Ojha, C. S. P. & Singh, V. P. 2008 Estimation of removal efficiency for settling basins using neural networks and support vector machines. J. Hydrol. Eng. ASCE 13, 146-155. (Pubitemid 351279631)
42. Sivapragasam, C. & Muttil, N. 2005 Discharge rating curve extension: a new approach. Water Resour. Manage. 19, 505-520. (Pubitemid 41600177)
43. Smola, A. J. 1996 Regression estimation with support vector learning machines. MSc Thesis, Technische Universität München, Germany.
44. Solomatine, D. P. & Dulal, K. N. 2003 Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrol. Sci. J. 48, 399-411.
45. Suykens, J. A. K., Van Gestel, T., Vandewalle, J. & De Moor, B. 2002 A support vector machine formulation to PCA analysis and its Kernel version. Internal Report 02-68, ESAT-SISTA, K.U. Leuven, Belgium.
46. Thissen, U., van Brakel, R., de Weijer, A. P., Melssen, W. J. & Buydens, L. M. C. 2003 Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems 69, 35-49. (Pubitemid 37324884)
47. Tripathi, S., Srinivas, V. V. & Nanjundian, R. S. 2006 Downscaling of precipitation for climate change scenarios: a support vector machine approach. J. Hydrol. 330, 621-640. (Pubitemid 44573197)
48. Vapnik, V. 1995 The Nature of Statistical Learning Theory. Springer, New York, 187 pp.
49. Vapnik, V. N. 1998 Statistical Learning Theory. John Wiley & Sons, New York, 740 pp.
50. Vapnik, V. 2000 The Nature of Statistical Learning Theory. Springer Verlag, New York, USA, 315 pp.
51. Vapnik, V., Golwich, S. & Smola, A. J. 1997 Support vector method for function approximation, regression estimation, and signal processing. In: Advances in Neural Information Processing Systems 9 (M. Mozer, M. Jordan & T. Petsche, eds). MIT Press, Cambridge, MA, USA, pp. 281-287.
52. Wilby, R. L., Abrahart, R. J. & Dawson, C. W. 2003 Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48, 163-181. (Pubitemid 36429884)
53. Wu, C. L., Chau, K. W. & Li, Y. S. 2008 River stage prediction based on a distributed support vector regression. J. Hydrol. 358, 96-111.
54. Zealand, C. M., Burn, D. H. & Simonovic, S. P. 1999 Short term stream flow forecasting using artificial neural networks. J. Hydrol. 214, 32-48. (Pubitemid 29087395)
55. Zhang, G. P. 2007 Avoiding pitfalls in neural network research. IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews 37, 3-16. (Pubitemid 46002485)