Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

Journal of Hydrology

Volumn 477, Issue , 2013, Pages 119-128

  Erdal, Halil Ibrahim ^a   Karakurt, Onur ^b  

^a Turkish Cooperation and Coordination Agency (TIKA)   (Turkey)

^b GAZI UNIVERSITY   (Turkey)

Author keywords

Bagging (bootstrap aggregating); Classification and regression trees; Ensemble learning; Stochastic gradient boosting; Streamflow prediction; Support vector regression

Indexed keywords

BAGGING (BOOTSTRAP AGGREGATING); CLASSIFICATION AND REGRESSION TREE; ENSEMBLE LEARNING; STOCHASTIC GRADIENT; STREAMFLOW PREDICTION; SUPPORT VECTOR REGRESSION (SVR);

FORESTRY; REGRESSION ANALYSIS; STREAM FLOW; WATER RESOURCES;

FORECASTING;

ACCURACY ASSESSMENT; BOOTSTRAPPING; ENSEMBLE FORECASTING; HYDROLOGICAL MODELING; LEARNING; PARADIGM SHIFT; PREDICTION; REGRESSION ANALYSIS; STOCHASTICITY; STREAMFLOW; WATER MANAGEMENT; WATER PLANNING; WATER RESOURCE;

CLASSIFICATION; FORECASTS; FORESTRY; REGRESSION ANALYSIS; STREAM FLOW; TREES; WATER RESOURCES;

EID: 84871477245     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2012.11.015     Document Type: Article

References (35)

1. Anctil F., Lauzon N. Generalisation for neural networks through data sampling and training procedures with applications to streamflow predictions. Hydrol. Earth Syst. Sci. 2004, 8(5):940-958. 10.5194/hess-8-940-2004.
2. Andriyashin, A., 2005. Financial Applications of Classification and Regression Trees. Master Thesis. Humboldt University. Berlin pp: 6-8.
3. Araghinejad S., Azmi M., Kholghi M. Application of artificial neural network ensembles in probabilistic hydrological forecasting. J. Hydrol. 2011, 407(1-4):94-104. 10.1016/j.jhydrol.2011.07.011.
4. Asefa T., Kemblowski M., Mckee M., Khalil A. Multi-time scale stream flow predictions: the support vector machines approach. J. Hydrol. 2006, 318(1-4):7-16. 10.1016/j.jhydrol.2005.06.001.
5. Boucher M.-A., Lalibert́e J.-P., Anctil F. An experiment on the evolution of an ensemble of neural networks for streamflow forecasting. Hydrol. Earth Syst. Sci. 2010, 14:603-612.
6. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, Wadsworth, Int. Group, Belmont, California, pp. 357-358.
7. Breiman L. Bagging predictors. Machine Learning 1996, 24(2):123-140. 10.1023/A:1018054314350.
8. Breiman, L., 1999. Using adaptive bagging to debias regressions. Technical Report No. 547 of University of California, Berkeley.
9. Bühlmann P., Yu B. Analyzing bagging. Ann. Stat. 2002, 30(4):927-961.
10. Cannon A.J., Whitfield P.H. Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J. Hydrol. 2002, 259:136-151. 10.1016/S0022-1694(01)00581-9.
11. Chou J.S., Chiu C.K., Farfoura M., Al-Taharwa I. Optimizing the prediction accuracy of concrete compressive strength based on a comparison of data-mining techniques. J. Comput. Civil Eng. 2011, 25(3):242-263. 10.1061/(ASCE)CP.1943-5487.0000088.
12. Efron B. Bootstrap methods: another look at the jackknife. Ann. Stat. 1979, 7(1):1-26. 10.1214/aos/1176344552.
13. Erdal H.I., Ekinci A. A comparison of various artificial intelligence methods in the prediction of bank failures. Comput. Econ. 2012, 10.1007/s10614-012-9332-0.
14. Friedman J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38(4):367-378. 10.1016/S0167-9473(01)00065-2.
15. Friedman J.H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001, 29(5):1189-1232.
16. Grunwald S., Daroub S.H., Lang T.A., Diaz O.A. Tree-based modeling of complex interactions of phosphorus loadings and environmental factors. Sci. Total Environ. 2009, 407(12):3772-3783.
17. Guo J., Zhou J., Qin H., Zou Q., Li Q. Monthly streamflow forecasting based on improved support vector machine model. Expert Syst. Appl. 2011, 38(10):13073-13081. 10.1016/j.eswa.2011.04.114.
18. Hancock T., Put R., Coomans D., Vanderheyden Y., Everingham Y. A performance comparison of modern statistical techniques for molecular descriptor selection and retention prediction in chromatographic QSRR studies. Chemomet. Intell. Lab. Syst. 2005, 76(2):185-196. 10.1016/j.chemolab.2004.11.001.
19. Hastie, T., Tibshirani, R., Friedman, J., 2001. The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer-Verlag, New York, p. 500. ISBN: 9780387848570.
20. Ismail R., Mutanga O. A comparison of regression tree ensembles: Predicting Sirexnoctilio induced water stress in Pinuspatula forests of KwaZulu-Natal, South Africa. Int. J. Appl. Earth Obs. Geoinf. 2010, 12S:S45-S51.
21. Jeong D.-I., Kim Y.-O. Rainfall-runoff models using artificial neural networks for ensemble streamflow prediction. Hydrol. Process. 2005, 19(19):3819-3835. 10.1002/hyp. 5983.
22. Kisi O., Cimen M. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. J. Hydrol. 2011, 399(1-2):132-140. 10.1016/j.jhydrol.2010.12.041.
23. Li P.-H., Kwon H.-H., Sun L., Lall U., Kao J.-J. A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, Taiwan. Int. J. Climatol. 2010, 30(8):1256-1268. 10.1002/joc.1954.
24. Pino-Mejias R., Jimenez-Gamero M.D., Cubiles-de-la-Vega M.D., Pascual-Acosta A. Reduced bootstrap aggregating of learning algorithms. Pattern Recogn. Lett. 2008, 29(3):265-271. 10.1016/j.patrec.2007.10.002.
25. Samsudin R., Saad P., Shabri A. A hybrid least squares support vector machines and GMDH approach for river flow forecasting. Hydrol. Earth Syst. Sci. Discuss 2010, 7:3691-3731. 10.5194/hessd-7-3691-2010.
26. Schapire E.R. The strength of weak learnability. Machine Learning 1990, 5:197-227. 10.1023/A:1022648800760.
27. Schölkopf B., Burges C.J.C., Smola A. Advances in Kernel Methods: Support Vector Learning 1999, MIT Press, Cambridge MA.
28. Shu C., Burn D.H. Artificial neural network ensembles and their application in pooled flood frequency analysis. Water Resour. Res. 2004, 40(W09301). 10.1029/2003WR002816.
29. Snelder T.H., Lamouroux N., Leathwick J.R., Pella H., Sauquet E., Shankar U. Predictive mapping of the natural flow regimes of France. J. Hydrol. 2009, 373:57-67. 10.1016/j.jhydrol.2009.04.011.
30. Tiwari M.K., Chatterjee C. A new Wavelet-Bootstrap-ANN hybrid model for daily discharge forecasting. J. Hydroinform. 2011, 13(3):500-519. 10.2166/hydro.2010.142.
31. Tiwari M.K., Chatterjee C. Development of an accurate and reliable hourly flood forecasting model using Wavelet-Bootstrap-ANN (WBANN) hybrid approach. J. Hydrol. 2010, 394(3-4):458-470. 10.1016/j.jhydrol.2010.10.001.
32. Vapnik V.N. The Nature of Statistical Learning Theory 1995, Springer-Verlag, New York.
33. Vezza P., Comoglio C., Rosso M., Viglione A. Low flows regionalization in North-Western Italy. Water Resour. Manage. 2010, 24:4049-4074. 10.1007/s11269-010-9647-3.
34. Wang G., Hao J., Mab J., Jiang H. A comparative assessment of ensemble learning for credit scoring. Exp. Syst. Appl. 2011, 38(1):223-230. 10.1016/j.eswa.2010.06.048.
35. Zhang C.X., Zhang J.S., Wang G.W. An empirical study of using rotation forest to improve regressors. Appl. Math. Comput. 2008, 195(2):618-629. 10.1016/j.amc.2007.05.010.