Effect of pruning and smoothing while using m5 model tree technique for reservoir inflow prediction

Journal of Hydrologic Engineering

Volumn 16, Issue 7, 2011, Pages 563-574

  Jothiprakash, V ^a   Kote, Alka S ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Hydrologic models; India; Inflow; Predictions; Reservoirs

Indexed keywords

AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE; DATA SETS; GOODNESS-OF-FIT MEASURE; HYDROLOGIC MODELS; IF-THEN RULES; INDIA; INFLOW; LOW FLOW; M5 MODEL TREE; MODEL VALIDATION; PARTICULAR DISTRIBUTION; PREDICTIONS; PREDICTIVE ACCURACY; RESERVOIR INFLOW PREDICTION; SCATTER PLOTS; TIME STEP; UNIVARIATE; ZERO VALUES;

FORECASTING; MEAN SQUARE ERROR; PLANT EXTRACTS; STOCHASTIC SYSTEMS; TIME SERIES;

STOCHASTIC MODELS;

DATA SET; HYDROLOGICAL MODELING; INFLOW; MODEL VALIDATION; PREDICTION; SMOOTHING; STOCHASTICITY; TIME SERIES ANALYSIS;

INDIA;

EID: 79960127215     PISSN: 10840699     EISSN: None     Source Type: Journal    
DOI: 10.1061/(ASCE)HE.1943-5584.0000342     Document Type: Article

References (38)

1. Ahmad S. Khan I.H. Parida B.P. Performance of stochastic approaches for forecasting river water quality. Water Resour. 2001, 35(18):4261-4266. WARED4, 0097-8078, 10.1016/S0043-1354(01)00167-1
2. Ahmad S. Simonovic S.P. An artificial neural network model for generating hydrograph from hydro-meteorological parameters. J. Hydrol. (Amsterdam) 2005, 315:236-251. JHYDA7, 0022-1694, 10.1016/j.jhydrol.2005.03.032
3. Ahmed J.A. Sarma A.K. Artificial neural network model for synthetic streamflow generation. Water Resour. Manage. 2007, 21(6):1015-1029. WRMAEJ, 0920-4741, 10.1007/s11269-006-9070-y
4. Akaike H. new look at the statistical model identification. IEEE Trans. Autom. Control 1974, 19(6):716-723. IETAA9, 0018-9286, 10.1109/TAC.1974.1100705
5. Bhattacharya B. Price R.K. Solomatine D.P. data mining approach to modeling sediment transport. Proc., 6th Int. Conf. on Hydroinformatics 2004, 1663-1670. World Scientific, Singapore.
6. Bhattacharya B. Solomatine D.P. Application of artificial neural networks and M5 model tree to modeling stage-discharge relationship. Proc., 2nd Int. Symp. on Flood Defence 2002, B. S. Beijing, Z. Y. Wu, G. Q. Wang, G. H. Wang, Huang, J. C. Huang, and eds., Science Press, New York.
7. Bhattacharya B. Solomatine D.P. Neural networks and M5 model tree in modeling water level-discharge relationship. Neurocomputing; Var. Star Bull. 2005, 63:381-396. NRCGEO, 0925-2312, 10.1016/j.neucom.2004.04.016
8. Bhattacharya B. Solomatine D.P. Improving empirical models with machine learning. Proc., 6th Int. Conf. on Neural Networks 2006, IEEE, Vancouver, BC, Canada.
9. Box G.E. P. Jenkins G.M. Time series analysis, forecasting and control 1976, and Holden Day, San Francisco.
10. Campolo M. Andreussi P. Soldati A. River flood forecasting with neural network model. Water Resour. Res. 1999, 35(4):1191-1197. WRERAQ, 0043-1397, 10.1029/1998WR900086
11. Chiang Y.M. Chang F.J. Integrating hydrometeorological information for rainfall-runoff modeling by artificial neural networks. Hydrol. Processes 2009, 23(11):1650-1659. 10.1002/hyp.7299, HYPRE3, 1099-1085
12. Coulibaly P. Anctil F. Bobee B. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J. Hydrol. (Amsterdam) 2000, 230:244-257. JHYDA7, 0022-1694, 10.1016/S0022-1694(00)00214-6
13. Coulibaly P. Hache M. Fortin V. Bobee B. Improving daily reservoir inflow forecasts with model combination. J. Hydrol. Eng. 2005, 10(2):91-99. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2005)10:2(91)
14. Hsu K.L. Gupta H.V. Sorooshian S. Artificial neural network modeling of the rainfall-runoff process. Water Resour. Res. 1995, 31(10):2517-2530. WRERAQ, 0043-1397, 10.1029/95WR01955
15. Ljung G.M. Box G.E. P. On measure of lack of fit in time series models. Biom. J. 1978, 65(2):297-303. BIJODN, 1521-4036
16. Makridakis S. Wheelwright S.C. Hyndman R.J. Forecasting: Methods and applications 1998, and Wiley, New York.
17. Minns A. Hall M. Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J. 1996, 41(3):399-417. HSJODN, 0262-6667, 10.1080/02626669609491511
18. Muluye G.Y. Coulibaly P. Seasonal reservoir inflow forecasting with low-frequency climatic indices: comparison of data-driven methods. Hydrol. Sci. J. 2007, 52(3):508-522. HSJODN, 0262-6667, 10.1623/hysj.52.3.508
19. Nash J.E. Sutcliffe J.V. River flow forecasting through conceptual models Part 1-A discussion of principles. J. Hydrol. (Amsterdam) 1970, 10(3):282-290. JHYDA7, 0022-1694, 10.1016/0022-1694(70)90255-6
20. Nokes D.J. Mcleod I. Hipel K.W. Forecasting monthly river flow time series. Int. J. Forecasting 1985, 1:179-190. IJFOEK, 0169-2070, 10.1016/0169-2070(85)90022-6
21. Preis A. Ostfeld A. hybrid (MT-LP) approach to water distribution systems inverse modeling. Proc., 2005 World Water and Environmental Resources Congress 2005, ASCE, Reston, VA.
22. Quinlan J.R. Learning with continuous classes. Proc., 5th Australian Joint Conf. on Artificial Intelligence 1992, 343-348. A. Adams, L. Sterling, and eds. World Scientific, Singapore.
23. Raghuwanshi N.S. Singh I. Reddy L.S. Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India. J. Hydrol. Eng. 2006, 11(1):71-79. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2006)11:1(71)
24. Salas J.D. Delleur V. Yevjevich V. Lane W.L. Applied modeling of hydrologic time series 1980, and Water Resources Publication, CO.
25. Senthilkumar A.R. Sudheer K.P. Jain S.K. Agarwal P.K. ANN based rainfall-runoff modeling: Comparison of network types. Hydrol. Processes 2005, 19(6):1277-1291. HYPRE3, 1099-1085, 10.1002/hyp.5581
26. Sivapragasam C. Vincent P. Vasudevan G. Genetic programming model for forecast of short and noisy data. Hydrol. Processes 2007, 21(2):266-272. HYPRE3, 1099-1085, 10.1002/hyp.6226
27. Solomatine D.P. Applications of data-driven modeling and machine learning in control of water resources. Computational intelligence in control of water resources 2002, 197-217. M. Mohammadian, R. A. Sarkar, X. Yao, (a). and eds., Idea Group Publishing, Hershey, PA.
28. Solomatine D.P. Data-driven modeling: Paradigm, methods, experiences. Proc., 5th Int. Conf. on Hydroinformatics 2002, (b). IWA Publishing, London.
29. Solomatine D.P. Local and hybrid learning models in forecasting natural phenomena. Proc., Int. IEEE Conf. on Neural Networks 2005, IEEE, Montreal, Canada.
30. Solomatine D.P. Dulal K.N. Model tree as an alternative to neural networks in rainfall-runoff modeling. Hydrol. Sci. J. 2003, 48(3):399-411. HSJODN, 0262-6667, 10.1623/hysj.48.3.399.45291
31. Solomatine D.P. Siek M.B. Flexibility and optimality in M5 model tree. Proc., 3rd Int. Conf. on Hybrid Intelligent Systems 2003, IOS Press, Amsterdam, Netherlands.
32. Solomatine D.P. Xue Y. M5 model tree and neural networks: Application to flood forecasting in the upper reach of the Huai River in China. J. Hydrol. Eng. 2004, 9(6):491-501. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2004)9:6(491)
33. Štravs L. Brilly M. Development of low-flow forecasting model using the M5 machine learning method. Hydrol. Sci. J. 2007, 52(3):466-477. HSJODN, 0262-6667, 10.1623/hysj.52.3.466
34. Sudheer K.P. Jain A. Explaining the internal behaviour of artificial neural network river flow models. Hydrol. Processes 2004, 18:833-844. HYPRE3, 1099-1085, 10.1002/hyp.5517
35. Sveinsson O.G. B. Forecasting spring reservoir inflows in Churchill Falls basin in Quebec, Canada. J. Hydrol. Eng. 2008, 13(6):426-437. JHYEFF, 1084-0699, 10.1061/(ASCE)1084-0699(2008)13:6(426)
36. Wang W. C. Chau K. W. Cheng C. T. Qiu L. comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol. (Amsterdam) 2009, 374(3-4):294-306. 10.1016/j.jhydrol.2009.06.019, JHYDA7, 0022-1694
37. Xu Z.X. Li J.Y. Short-term inflow forecasting using an artificial neural network model. Hydrol. Processes 2002, 16:2423-2439. HYPRE3, 1099-1085, 10.1002/hyp.1013
38. Yurekli K. Kurunc A. Simsek H. Prediction of daily maximum streamflow based on stochastic approaches. J. Spa. Hydrol. 2004, 4(2):1-12. 1530-4736