A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Journal of Hydrology

Volumn 374, Issue 3-4, 2009, Pages 294-306

  Wang, Wen Chuan ^a,b   Chau, Kwok Wing ^c   Cheng, Chun Tian ^b   Qiu, Lin ^a  

^a NORTH CHINA INSTITUTE OF WATER CONSERVANCY AND HYDROELECTRIC POWER   (China)

^b DALIAN UNIVERSITY OF TECHNOLOGY   (China)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

ANFIS; ANN; ARMA; GP; Monthly discharge time series forecasting; SVM

Indexed keywords

ANFIS; ANN; ARMA; GP; MONTHLY DISCHARGE TIME SERIES FORECASTING; SVM;

ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; FORECASTING; FUNCTION EVALUATION; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GENETIC PROGRAMMING; MATHEMATICAL MODELS; RECORDS MANAGEMENT; RESERVOIR MANAGEMENT; RESERVOIRS (WATER); SCHEDULING; SUPPORT VECTOR MACHINES; TIME SERIES; WATER; WATER ANALYSIS; WATER RESOURCES;

TIME SERIES ANALYSIS;

ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPARATIVE STUDY; DISCHARGE; FORECASTING METHOD; HYDROELECTRIC POWER; MACHINERY; MODEL VALIDATION; PERFORMANCE ASSESSMENT; RESERVOIR; TIME SERIES ANALYSIS;

EID: 68349105875     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2009.06.019     Document Type: Article

References (66)

1. Alvisi S., Mascellani G., Franchini M., and Bardossy A. Water level forecasting through fuzzy logic and artificial neural network approaches. Hydrology and Earth System Sciences 10 1 (2006) 1-17
2. Arena C., Cannarozzo M., and Mazzola M.R. Multi-year drought frequency analysis at multiple sites by operational hydrology - a comparison of methods. Physics and Chemistry of the Earth. Parts A/B/C 31 18 (2006) 1146-1163
3. ASCE Task Committee. Artificial neural networks in hydrology - I: preliminary concepts. Journal of Hydrologic Engineering, ASCE 5 2 (2000) 115-123
4. ASCE Task Committee. Artificial neural networks in hydrology - II: hydrological applications. Journal of Hydrologic Engineering, ASCE 5 2 (2000) 124-137
5. Asefa T., Kemblowski M., McKee M., and Khalil A. Multi-time scale stream flow predictions: the support vector machines approach. Journal of Hydrology 318 1-4 (2006) 7-16
6. Bazartseren B., Hildebrandt G., and Holz K.P. Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55 3-4 (2003) 439-450
7. Borrelli A., De Falco I., Della Cioppa A., Nicodemi M., and Trautteur G. Performance of genetic programming to extract the trend in noisy data series. Physica A - Statistical Mechanics and Its Applications 370 1 (2006) 104-108
8. Box G.E.P., and Jenkins G.M. Times Series Analysis Forecasting and Control (1970), Holden-Day, San Francisco
9. Campolo M., Soldati A., and Andreussi P. Artificial neural network approach to flood forecasting in the River Arno. Hydrological Sciences Journal 48 3 (2003) 381-398
10. Carlson R.F., MacCormick A.J.A., and Watts D.G. Application of linear random models to four annual streamflow series. Water Resources Research 6 4 (1970) 1070-1078
11. Chang L.C., and Chang F.J. Intelligent control for modelling of real-time reservoir operation. Hydrological Processes 15 9 (2001) 1621-1634
12. Chang F.J., Chang L.C., and Huang H.L. Real-time recurrent learning neural network for stream-flow forecasting. Hydrological Processes 16 13 (2002) 2577-2588
13. Chau K.W. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology 329 3-4 (2006) 363-367
14. Chau K.W., and Cheng C.T. Real-time prediction of water stage with artificial neural network approach. Lecture Notes in Artificial Intelligence 2557 (2002) 715
15. Chen H.L., and Rao A.R. Testing hydrologic time series for stationarity. Journal of Hydrologic Engineering 7 2 (2002) 129-136
16. Chen H.L., and Rao A.R. Linearity analysis on stationary segments of hydrologic time series. Journal of Hydrology 277 1-2 (2003) 89-99
17. Cheng C.T., Lin J.Y., Sun Y.G., and Chau K.W. Long-term prediction of discharges in Manwan Hydropower using adaptive-network-based fuzzy inference systems models. Advances in Natural Computation, Pt 3, Proceedings. Lecture Notes in Computer Science (2005), Springer-Verlag, Berlin pp. 1152-1161
18. Dibike Y.B., Velickov S., Solomatine D., and Abbott M.B. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering 15 3 (2001) 208-216
19. Dixon B. Applicability of neuro-fuzzy techniques in predicting ground-water vulnerability: a GIS-based sensitivity analysis. Journal of Hydrology 309 1-4 (2005) 17-38
20. Duan Q.Y., Gupta V.K., and Sorooshian S. Shuffled complex evolution approach for effective and efficient minimization. Journal of Optimization Theory and Applications 76 3 (1993) 501-521
21. Firat M., and Gungor M. Hydrological time-series modelling using an adaptive neuro-fuzzy inference system. Hydrological Processes 22 13 (2008) 2122-2132
22. Freiwan M., and Cigizoglu H.K. Prediction of total monthly rainfall in Jordan using feed forward back propagation method. Fresenius Environmental Bulletin 14 2 (2005) 142-151
23. Guven A., Aytek A., Yuce M.I., and Aksoy H. Genetic programming-based empirical model for daily reference evapotranspiration estimation. Clean-Soil Air Water 36 10-11 (2008) 905-912
24. Haykin S. Neural Networks: A Comprehensive Foundation. 2nd ed. (1999), Prentice-Hall, Upper Saddle River, NJ
25. Hipel K.W., and Mcleod A.I. Time series modelling of water resources and environmental systems (1994), Elsevier, Amsterdam pp. 463-465
26. Hu T.S., Lam K.C., and Ng S.T. River flow time series prediction with a range-dependent neural network. Hydrological Sciences Journal 46 5 (2001) 729-745
27. Jain A., and Kumar A.M. Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing 7 2 (2007) 585-592
28. Jang J.-S.R. ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man and Cybernetics 23 3 (1993) 665-685
29. Jang J.-S.R., Sun C.-T., and Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (1997), Prentice-Hall, Upper Saddle River, NJ
30. Johari A., Habibagahi G., and Ghahramani A. Prediction of soil-water characteristic curve using genetic programming. Journal of Geotechnical and Geoenvironmental Engineering 132 5 (2006) 661-665
31. Keskin M.E., Taylan D., and Terzi O. Adaptive neural-based fuzzy inference system (ANFIS) approach for modelling hydrological time series. Hydrological Sciences Journal 51 4 (2006) 588-598
32. Khan M.S., and Coulibaly P. Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering 11 3 (2006) 199-205
33. Komornik J., Komornikova M., Mesiar R., et al. Comparison of forecasting performance of nonlinear models of hydrological time series. Physics and Chemistry of the Earth. Parts A/B/C 31 18 (2006) 1127-1145
34. Koza J. Genetic Programming: On the Programming of Computers by Natural Selection (1992), MIT Press, Cambridge, MA
35. Legates D.R., and McCabe G.J. Evaluating the use of "goodness-of-fit" measures in hydrologic and hydroclimatic model validation. Water Resources Research 35 1 (1999) 233-241
36. Lin J.Y., Cheng C.T., and Chau K.W. Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal 51 4 (2006) 599-612
37. Liong S.Y., et al. Genetic Programming: a new paradigm in rainfall-runoff modeling. Journal of the American Water Resources Association 38 3 (2002) 705-718
38. Liong S.Y., and Sivapragasam C. Flood stage forecasting with support vector machines. Journal of the American Water Resources Association 38 1 (2002) 173-186
39. Loukas Y.L. Adaptive neuro-fuzzy inference system: an instant and architecture-free predictor for improved QSAR studies. Journal of Medicinal Chemistry 44 17 (2001) 2772-2783
40. Moller M.F. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6 4 (1993) 525-533
41. Moriasi D.N., et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE 50 3 (2007) 885-900
42. Muttil N., and Chau K.W. Neural network and genetic programming for modelling coastal algal blooms. International Journal of Environment and Pollution 28 3-4 (2006) 223-238
43. Nash J.E., and Sutcliffe J.V. River flow forecasting through conceptual models part I - a discussion of principles. Journal of Hydrology 10 3 (1970) 282-290
44. Nayak P.C., Sudheer K.P., Rangan D.M., and Ramasastri K.S. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology 291 1-2 (2004) 52-66
45. Nayak P.C., Sudheer K.P., Rangan D.M., and Ramasastri K.S. Short-term flood forecasting with a neuro-fuzzy model. Water Resources Research 41 4 (2005) (Art. NO. W04004)
46. Olsson J., Uvo C., Jinno B., et al. Neural networks for rainfall forecasting by atmospheric downscaling. Journal of Hydrologic Engineering 9 1 (2004) 1-12
47. Pai P.-F. System reliability forecasting by support vector machines with genetic algorithms. Mathematical and Computer Modelling 43 3-4 (2006) 262-274
48. Pai P.-F., and Hong W.-C. Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Conversion and Management 46 17 (2005) 2669-2688
49. Salas J.D. Analysis and modeling of hydrologic time series. In: Maidment D.R. (Ed). Handbook of Hydrology (1993), The McGraw Hill, New York 19.5-19.9
50. Santhi C., et al. Validation of tbe swat model on a large river basin with point and nonpoint sources. Journal of the American Water Resources Association 37 5 (2001) 1169-1188
51. Shu C., and Ouarda T.B.M.J. Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. Journal of Hydrology 349 1-2 (2008) 31-43
52. Silverman D., and Dracup J.A. Artificial neural networks and long-range precipitation prediction in California. Journal of Applied Meteorology 39 1 (2000) 57-66
53. Sivapragasam C., Vincent P., and Vasudevan G. Genetic programming model for forecast of short and noisy data. Hydrological Processes 21 2 (2007) 266-272
54. Srikanthan R., and McMahon T.A. Stochastic generation of annual, monthly and daily climate data: a review. Hydrology and Earth System Sciences 5 4 (2001) 653-670
55. Sudheer K.P., and Jain A. Explaining the internal behaviour of artificial neural network river flow models. Hydrological Processes 18 4 (2004) 833-844
56. Sudheer K.P., Gosain A.K., and Ramasastri K.S. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrological Processes 16 6 (2002) 1325-1330
57. Toth E., Brath A., and Montanari A. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 239 1-4 (2000) 132-147
58. Van Liew M.W., Arnold J.G., and Garbrecht J.D. Hydrologic simulation on agricultural watersheds: choosing between two models. Transactions of the ASAE 46 6 (2003) 1539-1551
59. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer, New York
60. Vapnik V. Statistical Learning Theory (1998), Wiley, New York
61. Wang T., Yang K.L., and Guo Y.X. Application of artificial neural networks to forecasting ice conditions of the Yellow River in the Inner Mongolia Reach. Journal of Hydrologic Engineering 13 9 (2008) 811-816
62. Whigam P.A., and Crapper P.F. Modelling rainfall-runoff relationships using genetic programming. Mathematical and Computer Modelling 33 (2001) 707-721
63. Wu C.L., Chau K.W., and Li Y.S. River stage prediction based on a distributed support vector regression. Journal of Hydrology 358 1-2 (2008) 96-111
64. Yu X.Y., Liong S.Y., and Babovic V. EC-SVM approach for real-time hydrologic forecasting. Journal of Hydroinformatics 6 3 (2004) 209-223
65. Yu P.S., Chen S.T., and Chang I.F. Support vector regression for real-time flood stage forecasting. Journal of Hydrology 328 3-4 (2006) 704-716
66. Zounemat-Kermani M., and Teshnehlab M. Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Applied Soft Computing 8 2 (2008) 928-936