Data mining methods for hydroclimatic forecasting

Advances in Water Resources

Volumn 34, Issue 11, 2011, Pages 1390-1400

  Wei, Wenge ^a   Watkins, David W ^a  

^a Michigan Technological University   (United States)

Author keywords

Classification tree; Large scale climate signals; Logistic regression tree; Piecewise linear regression; Seasonal streamflow forecasting

Indexed keywords

CLASSIFICATION TREES; LARGE-SCALE CLIMATE SIGNALS; LOGISTIC REGRESSION TREE; PIECEWISE LINEAR REGRESSION; SEASONAL STREAMFLOW FORECASTING;

ATMOSPHERIC PRESSURE; CLIMATE MODELS; DATA MINING; FORECASTING; NICKEL COMPOUNDS; PIECEWISE LINEAR TECHNIQUES; PLANT EXTRACTS; REGRESSION ANALYSIS; RESERVOIRS (WATER); STREAM FLOW; WATER MANAGEMENT; WATER SUPPLY;

TREES (MATHEMATICS);

DATA MINING; EL NINO-SOUTHERN OSCILLATION; FLOW MODELING; FORECASTING METHOD; HYDROMETEOROLOGY; NONLINEARITY; NORTH ATLANTIC OSCILLATION; PACIFIC DECADAL OSCILLATION; PROBABILITY; SEASONAL VARIATION; STREAMFLOW; TELECONNECTION;

COLORADO RIVER [NORTH AMERICA]; TEXAS; UNITED STATES;

EID: 80053175870     PISSN: 03091708     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.advwatres.2011.08.001     Document Type: Article

References (44)

1. Araghinejad S., Burn D.H., Karamouz M. Long-lead probabilistic forecasting of streamflow using oceanic-atmospheric and hydrological predictors. Water Resour Res 2006, 42:W07411. 10.1029/2004WR003853.
2. Armitage P. Tests for linear trends in proportions and frequencies. Biometrics 1955, 11:375-386.
3. Bessler T.F., Savic D.A., Walter G.A. Water reservoir control with data mining. J Water Resour Plng Mgmt 2003, 129(1):26-34.
4. Block P., Rajagopalan B. Interannual variability and ensemble forecast of the Upper Blue Nile Basin Kiremt season forecast. J Hydrometeorol 2007, 8:327-342.
5. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and regression trees 1984, Wadsworth, Belmont, California.
6. Caelli T., Caetano T.S. Graphical models for graph matching: approximate models and optimal algorithms. Pattern Recognit Lett 2005, 26(3):339-346.
7. Chan K.-Y., Loh W.-Y. LOTUS: an algorithm for building accurate and comprehensible logistic regression trees. J Comput Graph Statist 2004, 13(4):826-852.
8. Clair T.A., Ehrman J.M. Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers. Water Resour Res 1998, 34(3):1022-1031.
9. Cochran W.G. Some methods of strengthening the common χ2 tests. Biometrics 1954, 10:417-451.
10. Cook E.R., Seager R., Cane M.A., Stahle D.W. North American drought: reconstructions, causes, and consequences. Earth Sci Rev 2007, 8:93-134.
11. Coulibaly P., Anctil F., Bobée B. Multivariate reservoir inflow forecasting using temporal neural networks. J Hydrologic Eng ASCE 2001, 6(5):367-376.
12. Croley T.E. Using meteorology probability forecasts in operational hydrology 2000, ASCE Press, Reston, VA.
13. Dawson C.W., Wilby R.L. Hydrological modeling using artificial neural networks. Prog Phys Geogr 2001, 25:80-108.
14. Giustolisi O., Laucelli D. Improving generalization of artificial neural networks in rainfall-runoff modeling. Hydrol Sci J 2005, 50(3):439-457.
15. Govindaraju S.R. ASCE task committee on application of artificial neural networks in hydrology: artificial neural networks in hydrology, Parts I and II. J Hydrologic Eng ASCE 2001, 5(2):115-137.
16. Gray S.T., Graumlich L.J., Betancourt J.L., Pederson G.T. A tree-ring based reconstruction of the Atlantic Multidecadal Oscillation since 1567 AD. Geophys Res Lett 2004, 31:L12205. 10.1029/2004GL019932.
17. Hamlet A.F., Lettenmeier D.P. Columbia River streamflow forecasting based on ENSO and PDO climate signals. J Water Resour Plng Mgmt 1999, 125(6):333-334.
18. Han J., Kamber M. Data mining: concepts and techniques 2006, Morgan Kaufman. second ed.
19. Hand D.J., Mannila H., Smyth P. Principles of data mining 2001, The MIT Press, Cambridge, Mass.
20. Heyden Y.V., Popovici S.T., Schoenmakers P.J. Evaluation of size-exclusion chromatography and size-exclusion electrochromatography calibration curves. J Chromatogr A 2002, 957(2):127-137.
21. Katz R.W., Murphy A.H. Economic value of weather and climate forecasts 1997, Cambridge University Press, Cambridge, United Kingdom, (Ch. 2, pp. 19-74).
22. Kerr R.A. A North Atlantic climate pacemaker for the centuries. Science 2000, 228:1984-1986.
23. Kutner H.M., Nachtsheim J.C., Neter J. Applied linear regression models 2004, McGraw-Hill, New York.
24. Lim, T-S, Loh, W-Y, Shih, Y-S. An empirical comparison of decision trees and other classification methods. Technical report 979, Department of Statistics, University of Wisconsin, Madison, Wis; 1997.
25. Maier H.R., Dandy G.C. Use of artificial neural networks for prediction of water quality parameters. Water Resour Res 1996, 32(4):1022-1031.
26. Mantua N.J., Hare S.R., Zhang Y., Wallace J.M., Francis R.C. A pacific interdecadal climate oscillation with impacts on salmon production. Bull Am Meteorol Soc 1997, 78:1069-1079.
27. McCullagh P., Nelder J.A. Generalized linear models 1989, Chapman & Hall, London. 2nd ed.
28. McLachlan J.G. Discriminant analysis and statistical pattern recognition 1992, John Wiley & Sons, Inc., Hoboken, New Jersey.
29. van Oldenborgh G.J., Burgers G. Searching for decadal variations in ENSO precipitation teleconnections. Geophys Res Lett 2005, 32(15):L15701. 10.1029/2005GL023110.
30. Philander S.G. El Niño, La Niña and the Southern Oscillation 1990, Elsevier, New York.
31. Piechota T.C., Chiew F.H.S., Dracup J.A., McMahon T.A. Development of an exceedance probability streamflow forecast. J Hydrologic Eng 2001, 6(1):20-28.
32. Piechota T.C., Chiew F.H.S., Dracup J.A., McMahon T.A. Seasonal streamflow forecasting in eastern Australia and the El Nino-Southern oscillation. Water Resour Res 1998, 34:3035-3044.
33. Put R., Perrin C., Questier F., Coomans D., Massart D.L., Vander Heyden Y. Classification and regression tree analysis for molecular descriptor selection and retention prediction in chromatographic quantitative structure-retention relationship studies. J Chromatogr A 2003, 988(2):261-276.
34. Ripley B.D. Pattern recognition and neural networks 1996, Cambridge University Press, Cambridge.
35. Shamseldin A.Y. Application of neural network technique to rainfall-runoff modeling. J Hydrol 1997, 199:272-294.
36. Sharma A. Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: 1. A strategy for system predictor identification. J Hydrol 2000, 239:249-258.
37. Smith T.M., Reynolds R.W., Peterson T.C., Lawrimore J. Improvements to NOAA's historical merged land-ocean surface temperature analysis (1880-2006). J Climate 2008, 21:2283-2296.
38. Stedinger J.R., Kim Y.-O. Probabilities of ensemble forecasts reflecting climate information. J Hydrol 2010, 391:9-23.
39. Steinberg D., Colla P. CART-classification and regression trees 1997, Salford Syst., San Diego, CA.
40. Stephenson D.B. Glossary. Forecast verification: a practitioner's guide in atmospheric science 2003, Wiley and Sons Ltd., UK. I.T. Jolliffe, D.B. Stephenson (Eds.).
41. Tootle G.A., Piechota T.C. Relationships between Pacific and Atlantic Ocean sea surface temperatures and US streamflow variability. Water Resour Res 2006, 42:W07411. 10.1029/2005WR004184.
42. Tu J.V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996, 49:1225-1231.
43. Wei W., Watkins D.W. Probabilistic streamflow forecasts based on hydrologic persistence and large-scale climate signals in central texas. J Hydroinf 2011, 13:760-774.
44. Wurbs RA. Fundamentals of water availability modeling with WRAP. Technical report 283, Texas Water Resources Institute, College Station, Tex; 2008.