Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

Journal of Hydrology

Volumn 389, Issue 1-2, 2010, Pages 146-167

  Wu, C L ^a,b   Chau, K W ^a   Fan, C ^c  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^b CHANGJIANG WATER RESOURCES COMMISSION   (China)

^c RYERSON UNIVERSITY   (Canada)

Author keywords

Fuzzy C means clustering; Modular artificial neural network; Moving average; Principal component analysis; Rainfall prediction; Singular spectral analysis

Indexed keywords

FUZZY C MEANS CLUSTERING; MODULAR ARTIFICIAL NEURAL NETWORKS; MOVING AVERAGE; MOVING AVERAGES; RAINFALL PREDICTION; SPECTRAL ANALYSIS;

CLUSTER ANALYSIS; FORECASTING; FUZZY CLUSTERING; FUZZY SYSTEMS; NEURAL NETWORKS; OPTIMIZATION; RAIN; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS; TIME SERIES;

PRINCIPAL COMPONENT ANALYSIS;

ARTIFICIAL NEURAL NETWORK; CLIMATE PREDICTION; CORRECTION; DATA PROCESSING; FUZZY MATHEMATICS; PRINCIPAL COMPONENT ANALYSIS; RAINFALL; SPECTRAL ANALYSIS; TIME SERIES;

EID: 77954384622     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2010.05.040     Document Type: Article

References (74)

1. Abrahart R.J., See L.M. Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments. Hydrology and Earth System Sciences 2002, 6(4):655-670.
2. Baratta D., Cicioni G., Masulli F., Studer L. Application of an ensemble technique based on singular spectrum analysis to daily rainfall forecasting. Neural Networks 2003, 16:375-387.
3. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Plenum Press, New York.
4. Brath A., Montanari A., Toth E. Neural networks and non-parametric methods for improving realtime flood forecasting through conceptual hydrological models. Hydrology and Earth System Sciences 2002, 6(4):627-640.
5. Campolo M., Andreussi P., Soldati A. Artificial neural network approach to flood forecasting in the river Arno. Hydrological Sciences Journal 2003, 48(3):381-398.
6. Cannas, B., Fanni, A., Pintus, M., Sechi, G.M., 2002. Neural Network Models to Forecast Hydrological Risk. IEEE IJCNN, 2002.
7. Chan J.C.L., Shi J.E. Prediction of the summer monsoon rainfall over South China. International Journal of Climatology 1999, 19(11):1255-1265.
8. Chattopadhyay S., Chattopadhyay G. Identification of the best hidden layer size for three-layered neural net in predicting monsoon rainfall in India. Journal of Hydroinformatics 2007, 10(2):181-188.
9. Cheng B., Titterington D.M. Titterington neural networks: a review from a statistical perspective. Statistical Science 1994, 9(1):2-54.
10. Chu P.S., He Y.X. Long-range prediction of Hawaiian winter rainfall using canonical correlation-analysis). International Journal of Climatology 1994, 14(6):659-669.
11. Corzo G., Solomatine D. Base flow separation techniques for modular artificial neural network modelling in flow forecasting. Hydrological Sciences Journal - des Sciences Hydrologiques 2007, 52(3):491-507.
12. Coulibaly P., Haché M., Fortin V., Bobée B. Improving daily reservoir inflow forecasts with model combination. Journal of Hydrologic Engineering 2005, 10(2):91-99.
13. Dawson C.W., Wilby R.L. Hydrological modeling using artificial neural networks. Progress in Physical Geography 2001, 25(1):80-108.
14. de Vos N.J., Rientjes T.H.M. Constraints of artificial neural networks for rainfall-runoff modeling: trade-offs in hydrological state representation and model evaluation. Hydrology and Earth System Sciences 2005, 9:111-126.
15. DelSole T., Shukla J. Linear prediction of Indian monsoon rainfall. Journal of Climate 2002, 15(24):3645-3658.
16. Diomede T., Davolio S., Marsigli C., Miglietta M.M., Moscatello A., Papetti P., Paccagnella T., Buzzi A., Malguzzi P. Discharge prediction based on multi-model precipitation forecasts. Meteorology and Atmospheric Physics 2008, 101(3-4):245-265.
17. Draper N.R., Smith H. Applied Regression Analysis 1998, Wiley, New York. third ed.
18. Fortin V., Ouarda T.B.M.J., Bobée B. Comment on 'the use of artificial neural networks for the prediction of water quality parameters' by H.R. Maier and G.C. Dandy. Water Resources Research 1997, 33(10):2423-2424.
19. Fraser A.M., Swinney H.L. Independent coordinates for strange attractors from mutual information. Physical Review A 1986, 33(2):1134-1140.
20. Ganguly A.R., Bras R.L. Distributed quantitative precipitation forecasting (DQPF) using information from radar and numerical weather prediction models. Journal of Hydrometeorology 2003, 4(6):1168-1180.
21. Giustolisi O., Savic D.A. A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics 2006, 8(3):207-222.
22. Golyandina N., Nekrutkin V., Zhigljavsky A. Analysis of Time Series Structure: SSA and Related Techniques 2001, Chapman & Hall/CRC, Berlin.
23. Guhathakurta P. Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model. Meteorology and Atmospheric Physics 2008, 101(1-2):93-108.
24. Hotelling H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 1933, 24:417-441.
25. Hsu K.L., Gupta H.V., Gao X.G., Sorooshian S., Imam B. Self-organizing linear output map (SOLO): an artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research 2002, 38(12):1302. doi:10.1029/2001WR000795.
26. Hu T.S., Lam K.C., Ng S.T. River flow time series prediction with a range-dependent neural network. Hydrological Science Journal 2001, 46(5):729-745.
27. Hu T.S., Wu F.Y., Zhang X. Rainfall-runoff modeling using principal component analysis and neural network. Nordic Hydrology 2007, 38(3):235-248.
28. Jain A., Srinivasulu S. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. Journal of Hydrology 2006, 317:291-306.
29. Jayawardena A.W., Lai F. Analysis and prediction of chaos in rainfall and stream flow time series. Journal of hydrology 1994, 153:23-52.
30. Kennel M.B., Brown R., Abarbanel H.D.I. Determining embedding dimension for phase space reconstruction using geometrical construction. Physical Review A 1992, 45(6):3403-3411.
31. Kim T., Heo J.H., Jeong C.S. Multireservoir system optimization in the Han River basin using multi-objective genetic algorithms. Hydrological Processes 2006, 20(9):2057-2075.
32. Kitanidis P.K., Bras R.L. Real-time forecasting with a conceptual hydrologic model, 2, applications and results. Water Resources Research 1980, 16(6):1034-1044.
33. Lee C.F., Lee J.C., Lee A.C. Statistics for Business and Financial Economics (2nd Version) 2000, World Scientific, Singapore.
34. Legates D.R., McCabe G.J. Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resources Research 1999, 35(1):233-241.
35. Li F., Zeng Q.C. Statistical prediction of East Asian summer monsoon rainfall based on SST and sea ice concentration. Journal of the Meteorological Society of Japan 2008, 86(1):237-243.
36. Lin G.F., Chen L.H. Application of artificial neural network to typhoon rainfall forecasting. Hydrological Processes 2005, 19(9):1825-1837.
37. Lin G.F., Wu M.C. A hybrid neural network model for typhoon-rainfall forecasting. Journal of Hydrology 2009, 375(3-4):450-458.
38. Lin G.F., Chen G.R., Wu M.C., Chou Y.C. Effective forecasting of hourly typhoon rainfall using support vector machines. Water Resources Research 2009, 45:W08440. 10.1029/2009WR007911.
39. Lisi F., Nicolis O., Sandri M. Combining singular-spectrum analysis and neural networks for time series forecasting. Neural Processing Letters 1995, 2(4):6-10.
40. Marques C.A.F., Ferreira J., Rocha A., Castanheira J., Gonçalves P., Vaz N., Dias J.M. Singular spectral analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth 2006, 31:1172-1179.
41. May R.J., Maier H.R., Dandy G.C., Fernando T.M.K. Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modeling & Software 2008, 23:1312-1328.
42. McCuen R.H. Hydrologic Analysis and Design 2005, Pearson/Prentice Hall, Upper Saddle River, NJ. third ed.
43. Munot A.A., Kumar K.K. Long range prediction of Indian summer monsoon rainfall. Journal of Earth System Science 2007, 116(1):73-79.
44. Nash J.E., Sutcliffe J.V. River flow forecasting through conceptual models part I - a discussion of principles. Journal of Hydrology 1970, 10(3):282-290.
45. Nayagam L.R., Janardanan R., Mohan H.S.R. An empirical model for the seasonal prediction of southwest monsoon rainfall over Kerala, a meteorological subdivision of India. International Journal of Climatology 2008, 28(6):823-831.
46. Newbold P., Carlson W.L., Thorne B.M. Statistics for Business and Economics (Fifth Version) 2003, Prentice Hall, Upper Saddle River, NJ.
47. Partal T., Kişi Ö. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology 2007, 342(2):199-212.
48. Pearson K. On lines and planes of closest fit to systems of points in space. Philosophical Magazine 1901, 2:559-572.
49. Pongracz R., Bartholy J., Bogardi I. Fuzzy rule-based prediction of monthly precipitation. Physics and Chemistry of the Earth Part B - Hydrology Oceans and Atmosphere 2001, 26(9):663-667.
50. Rajurkar M.P., Kothyari U.C., Chaube U.C. Artificial neural networks for daily rainfall-runoff modeling. Hydrological Sciences Journal 2002, 47(6):865-876.
51. Applied Modeling of Hydrologic Time Series 1985, Water Resources Publications, Littleton, Colorado. J.D. Salas, J.W. Delleur, V. Yevjevich, W.L. Lane (Eds.).
52. See L., Openshaw S. A hybrid multi-model approach to river level forecasting. Hydrological Sciences Journal 2000, 45(4):523-536.
53. Shamseldin A.Y., O'Connor K.M. A real-time combination method for the outputs of different rainfall-runoff models. Hydrological Sciences Journal 1999, 44(6):895-912.
54. Shamseldin A.Y., O'Connor K.M., Liang G.C. Methods for combining the outputs of different rainfall runoff models. Journal of Hydrology 1997, 197:203-229.
55. Sheng C., Gao S., Xue M. Short-range prediction of a heavy precipitation event by assimilating Chinese CINRAD-SA radar reflectivity data using complex cloud analysis. Meteorology and Atmospheric Physics 2006, 94(1-4):167-183.
56. Shrestha D.L., Solomatine D.P. Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 2006, 19(2):225-235.
57. Silverman D., Dracup J.A. Artificial neural networks and long-range precipitation in California. Journal of Applied Meteorology 2000, 31(1):57-66.
58. Sivapragasam C., Liong S.Y. Flow categorization model for improving forecasting. Nordic Hydrology 2005, 36(1):37-48.
59. Sivapragasam C., Liong S.Y., Pasha M.F.K. Rainfall and runoff forecasting with SSA-SVM approach. Journal of Hydroinformatics 2001, 3(7):141-152.
60. Sivapragasam C., Vincent P., Vasudevan G. Genetic programming model for forecast of short and noisy data. Hydrological Processes 2007, 21:266-272.
61. Solomatine D.P., Ostfeld A. Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics 2008, 10(1):3-22.
62. Solomatine D.P., Xue Y.I. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrological Engineering 2004, 9(6):491-501.
63. Sudheer K.P., Gosain A.K., Ramasastri K.S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes 2002, 16:1325-1330.
64. Sugihara G., May R.M. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature 1990, 344:734-741.
65. Theiler J. Spurious dimension from correlation algorithms applied to limited time-series data. Physical Review A 1986, 34(3):2427-2432.
66. Toth E., Brath A., Montanari A. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 2000, 239:132-147.
67. Venkatesan C., Raskar S.D., Tambe S.S., Kulkarni B.D., Keshavamurty R.N. Prediction of all India summer monsoon rainfall using error-back-propagation neural networks. Meteorology and Atmospheric Physics 1997, 62(3-4):225-240.
68. Wang W., Van Gelder P.H.A.J.M., Vrijling J.K., Ma J. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology 2006, 324:383-399.
69. Wu C.L., Chau K.W., Li Y.S. River stage prediction based on a distributed support vector regression. Journal of Hydrology 2008, 358:96-111.
70. Wu C.L., Chau K.W., Li Y.S. Methods to improve neural network performance in daily flows prediction. Journal of Hydrology 2009, 372(1-4):80-93.
71. Xiong L.H., Shamseldin A.Y., O'Connor K.M. A non-linear combination of forecasts of rainfall-runoff models by the first-order TS fussy system for forecast of rainfall-runoff model. Journal of Hydrology 2001, 245:196-217.
72. Yates D.N., Warner T.T., Leavesley G.H. Prediction of a flash flood in complex terrain. Part II: A comparison of flood discharge simulations using rainfall input from radar, a dynamic model, and an automated algorithmic system. Journal of Applied Meteorology 2000, 39(6):815-825.
73. Yu D.L., Gomm J.B., Williams D. Neural model input selection for a MIMO chemical process. Engineering Applications of Artificial Intelligence 2000, 13(1):15-23.
74. Zhang B., Govindaraju R.S. Prediction of watershed runoff using Bayesian concepts and modular neural networks. Water Resources Research 2000, 36(3):753-762.