Instance-based learning compared to other data-driven methods in hydrological forecasting

Hydrological Processes

Volumn 22, Issue 2, 2008, Pages 275-287

  Solomatine, Dimitri P ^a   Maskey, Mahesh ^b   Shrestha, Durga Lal ^a  

^a Institute for Water Education   (Netherlands)

^b NepalConsult (P) Ltd   (Nepal)

Author keywords

Artificial neural networks; Data driven models; Floods; Hydrological modelling; Instance based learning; k nearest neighbour method; Locally weighted regression

Indexed keywords

COMPUTER SIMULATION; DATA PROCESSING; FLOODS; FORECASTING; LEARNING SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS;

DATA-DRIVEN MODEL; HYDROLOGICAL MODELLING; INSTANCE-BASED LEARNING; LOCALLY WEIGHTED REGRESSION;

CATCHMENTS;

CATCHMENTS; COMPUTER SIMULATION; DATA PROCESSING; FLOODS; FORECASTING; LEARNING SYSTEMS; NEURAL NETWORKS; REGRESSION ANALYSIS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CATCHMENT; CONCEPTUAL FRAMEWORK; FLOOD FORECASTING; FLOW MODELING; GAUSSIAN METHOD; HYDROLOGICAL MODELING;

EID: 38549089135     PISSN: 08856087     EISSN: 10991085     Source Type: Journal    
DOI: 10.1002/hyp.6592     Document Type: Article

References (43)

1. Abrahart RJ, See L. 2000. Comparing neural network and autoregressive moving average techniques for the provision of continous river flow forecasts in two contrasting catchments. Hydrological Processes 14: 2157-2172.
2. Aha D, Kibler D, Albert M. 1991. Instance-based learning algorithms. Machine Learning 6: 37-66.
3. Allen RG, Pereira SL, Raes D, Smith M. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO: Rome.
4. Atkeson CG, Moorey AW, Schaal S. 1996. Locally weighted learning. Artificial Intelligence Review 11: 11-73.
5. Becker A, Kundzewicz ZW. 1987. Nonlinear flood routing with multilinear models. Water Resources Research 23: 1043-1048.
6. Bray M, Han D. 2004. Identification of support vector machines for runoff modelling. Journal of Hydroinformatics 6: 265-280.
7. Cigizoglu HK. 2003. Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences Journal 48(3): 349-361.
8. Cleveland WS, Loader C. 1994. Smoothing by Local Regression: Principles and Methods, Technical Report 95-3. AT and T Bell Laboratories, Statistics Department: Murray Hill, NJ.
9. Cover TM, Hart PE. 1967. Nearest neighbour pattern classification. IEEE Transactions on Information Theory 13: 21-27.
10. Dawson CW, Wilby RL. 2001. Hydrological modeling using artificial neural networks. Progress in Physical Geography 25(1): 80-108.
11. Dibike Y, Solomatine DP. 1999. River Flow Forecasting using Artificial Neural Networks. Geophysical Research Abstracts, EGS XXIV General Assembly: The Hague.
12. Dibike Y, Solomatine DP. 2001. River flow forecasting using artificial neural networks. Journal of Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere 26(1): 1-8.
13. Fedorov VV, Hackl P, Muller WG. 1993. Moving local regression: the weight function. Nonparametric Statistics 2(4): 355-368.
14. Franchini M. 1996. Use of genetic algorithm combined with a local search method for the automatic calibration of conceptual rainfall-runoff models. Hydrological Science Journal 41(1l): 21-39.
15. Galeati G. 1990. A comparison of parametric and non-parametric methods for runoff forecasting. Hydrological Sciences Journal 35(1): 79-94.
16. Gasser T, Muller HG. 1979. Kernel estimation of regression functions. In Smoothing Techniques for Curve Estimation, Lecture Notes in Mathematics, Gasser T, Rosenblatt M, (eds) Springer-Verlag: Heidelberg; 23-67.
17. Govindaraju RS, Rao AR. 2000. Artificial Neural Networks in Hydrology. Kluwer Academic Publishers: Norwell, MA; 329.
18. Haykin S. 1999. Neural Networks: A Comprehensive Foundation. Prentice Hall: Englewood Cliffs, NJ.
19. Hsu K, Gupta HV, Sorooshian S. 1995. Artificial neural network modelling of the rainfall-runoff process. Water Resources Research 31(10): 2517-2530.
20. Karlsson M, Yakowitz S. 1987. Nearest neighbour methods for non-parametric rainfall-runoff forecasting. Water Resources Research 23(7): 1300-1308.
21. Kuncheva LI. 2004. Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons: Chichester.
22. Maier HR, Dandy GC. 2000. Neural network for the prediction and forecasting of water resources variables: a review of modeling issues and application. Environmental Modeling & Software 15: 101-124.
23. Marsigli M, Todini F, Diomede T, Liu Z, Vignoli R. 2002. Calibration of rainfall-runoff models. MUSIC-Multiple-Sensor Precipitation Measurements, Integration, Calibration and Flood Forecasting. Brussels: Commission European Communities.
24. Minns AW, Hall MJ. 1996. Artificial neural networks as rainfall-runoff models. Hydrolgical Science Journal 41: 399-417.
25. Mitchell TM. 1997. Machine Learning. McGraw-Hill: Singapore; 414.
26. Quinlan JR. 1992. Learning with continuous classes. In The 5th Australian Joint Conference on AI, World Scientific: London; 343-348.
27. Quinlan JR. 1993. Combining instance-based and model-based learning. In Machine Learning: Proceedings of the Tenth International Conference. Morgan Kaufmann: Amherst, MA; 236-243.
28. Refsgaard JC. 1996. Terminology, modelling protocol and classification of hydrological model codes. In Distributed Hydrological Modelling, Abbott MB, Refsgaard JC (eds). Kluwer Academic Publishers: Dordrecht; 321.
29. Scott DW. 1992. Multivariate Density Estimation. Wiley: New York.
30. Shamseldin AY, O'Connor KM. 1996. A nearest neighbour linear perturbation model for river flow forecasting. Journal of Hydrology 179: 353-375.
31. Shrestha I. 2003. Conceptual and data-driven hydrological modelling of bagmati river basin, Nepal. MSc thesis HH451, UNESCO-IHE Institute for Water Education, Delft.
32. Shrestha DL, Solomatine DP. 2006. Machine learning approaches for estimation of prediction interval for the model output. Neural Networks 19(2): 225-235.
33. Solomatine DP. 2005. Data-driven modeling and computational intelligence methods in hydrology. In Encyclopedia of Hydrological Sciences, Anderson M (ed.). Wiley: New York.
34. Solomatine DP, Dulal KN. 2003. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal 48(3): 399-411.
35. Solomatine DP, Price RK. 2004. Innovative approaches to flood forecasting using data driven and hybrid modelling. In 6th International Conference on Hydroinformatics. World Scientific Publishing Company: Singapore.
36. Solomatine DP, Siek MB. 2006. Modular learning models in forecasting natural phenomena. Neural Networks 19(2): 215-224.
37. Solomatine DP, Xue Y. 2004. M5 model trees and neural networks: application flood forecasting in the upper reach the Huai River in China. Journal of Hydrologic Engineering 9(6).
38. Sugawara M. 1995. Tank model. In Computer Models of Watershed Hydrology, Singh VP (ed.). Water Resources Publication: Highlands Ranch, CO; 165-214.
39. Todini E. 1996. The arno rainfall-runoff model. Journal of Hydrology, 175: 339-382.
40. Toth E, Brath A, Montanari A. 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 239: 132-147.
41. Weiss SM, Indurkhya N. 1995. Rule-based machine learning methods for functional prediction. Journal of Artificial Intelligence Research 3: 383-403.
42. Witten IH, Frank E. 2000. Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmaan: San Francisco, CA; 132-147.
43. Wolpert D. 1992. Stacked generalisation. Neural Networks 5: 241-259.