Comparison between active learning method and support vector machine for runoff modeling

Journal of Hydrology and Hydromechanics

Volumn 60, Issue 1, 2012, Pages 16-32

  Shahraiyni, Hamid ^a   Ghafouri, Mohammad ^b   Shouraki, Saeed ^c   Saghafian, Bahram ^d   Nasseri, Mohsen ^e  

^a TARBIAT MODARES UNIVERSITY   (Iran)

^b SHAHROOD UNIVERSITY OF TECHNOLOGY   (Iran)

^c SHARIF UNIVERSITY OF TECHNOLOGY   (Iran)

^d Ministry Of Agricultural Jahad Islamic Republic of Iran   (Iran)

^e UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Active Learning Method (ALM); Fuzzy Modeling; Genetic Algorithm; Karoon River Basin; Runoff Modeling; Support Vector Machine (SVM)

Indexed keywords

ACTIVE LEARNING METHODS; FUZZY MODELING; KAROON RIVER; RUNOFF MODELING; SUPPORT VECTOR MACHINE (SVM);

COMPUTER SIMULATION; GENETIC ALGORITHMS; OPTIMIZATION; RUNOFF;

SUPPORT VECTOR MACHINES;

EID: 84858250001     PISSN: 0042790X     EISSN: 13384333     Source Type: Journal    
DOI: 10.2478/v10098-012-0002-7     Document Type: Article

References (66)

1. ABBASS H. A., SARKER R. A., and NEWTON C. S., 2002: Data mining: A heuristic approach. Idea Group Publishing.
2. ANVARI TAFTI S., 2008: Upgrading intelligent models for streamflow forecasting using distributed climatic data and snow cover. MS thesis, Faculty of Agriculture, Tarbiat Modares University, Iran.
3. ASEFA T., KEMBLOWSKI M. W., MCKEE M., and KHALIL A., 2006: Multi-time scale stream flow prediction: The support vector machines approach. J. Hydrol., 318, 7-16.
4. BAGHERI SHOURAKI S. and HONDA N., 1997: A new method for establishing and saving fuzzy membership functions. Proc., Int. 13th Fuzzy Symposium, 4-6 June, Toyama, Japan, pp. 91-93.
5. BAGHERI SHOURAKI S., and HONDA N., 1999: Recursive fuzzy modeling based on fuzzy interpolation. J. Advanced Computational Intelligence, 3, 114-125.
6. BATTITI R., 1994: Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks, 5, 4, 537-550.
7. BEHZAD M., ASGHARI K., EAZI M., PALHANG M., 2009: Generalization performance of support vector machines and neural networks in runoff modeling, Expert Systems with Applications, 36, 7624-7629.
8. BOWDEN G. J., MAIER H. R., and DANDY G. C., 2002: Optimal division of data for neural network models in water resources applications. Water Resour. Res., 38, 2, 1010, doi: 10.1029/2001WR000266. (http://www.agu.org/pubs/crossref/ 2002/2001WR000266.shtml)
9. BOWDEN G. J. DANDY G. C. & MAIER, H. R., 2005a: Input determination for neural network models in water resources applications, Part 1-background and methodology. J. Hydrol., 301, 75-92.
10. BOWDEN, G. J. DANDY G. C. & MAIER, H. R. (2005b): Input determination for neural network models in water resources applications, Part 2. Case study: Forecasting salinity in a river. J. Hydrol., 301, 93-107.
11. BOSER B. E., GUYON I. M., VAPNIK V. N., 1992: A training algorithm for optimal margin classifiers. In D. Haussler, editor, 5th Annual ACM Workshop on COLT, 144-152, Pittsburgh, PA, ACM Press.
12. CHAMBERS J. M., CLEVELAND W. S., KLEINER B., TUKEY P. A., 1983: Graphical methods for data analysis. Wadworth, Belmont, California, pp. 395.
13. CHANG C. C and LIN C. J., 2011: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1-27:27. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
14. CHANG F. J. and CHANG Y. T., 2006: Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Res, 29, 1-10.
15. CHANG F. J. and CHEN Y. C., 2001: Counter propagation fuzzy-neural network modeling approach to real time streamflow prediction. J. Hydrol., 245, 153-163.
16. CHAU K. W., WU C. L. and LI Y. S., 2005: Comparison of several flood forecasting models in Yangtze River. J. Hydrologic Engng., ASCE, 10, 6, 485-491.
17. CHEN S. H., LIN Y. H., CHANG L. C., and CHANG F. J., 2006: The strategy of building a flood forecast model by neuro-fuzzy network. Hydrol. Process., 20, 1525-1540.
18. CHENG C. T., OU C. P. and CHAU K. W., 2002: Combining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration. J. Hydrol., 268, 72-86.
19. DIBIKE Y. B., VELICKOV S., SOLOMATINE D., and ABBOTT M. B., 2001: Model Induction with support vector machines: Introduction and Application. J. Computing in Civil Engng., 15, 3, 208-216.
20. DUAN D., FERMINI B., and NATTELI S., 1992: Sustained Outward Current Observed after Ito1 Inactivation in Rabbit Atrial Myocytes Is a Novel Cl- current". Am. J. Physiol., 263, H1967-1971.
21. FAUSSET L., 1994: Fundamentals of neural networks: Architectures, algorithms and applications. Prentice Hall, 461p.
22. FERNANDO T. M. K. G., MAIER H. R., DANDY G. C., 2009: Selection of input variables for data driven models: An average shifted histogram partial mutual information estimator approach. J. Hydrol., 367, 3-4, 165-176.
23. FIRAT M., and GUNGOR M., 2006: River flow estimation using adaptive neuro fuzzy inference system. Math. Compute. Simulate., 75, 3-4, 87-96.
24. FIRAT M., 2008: Comparison of artificial intelligence techniques for river flow forecasting. Hydrol. Earth Syst. Sci., 12, 123-139.
25. HSU K., GUPTA H. V., and SOROOSHIAN S., 1995: Artificial neural network modeling of rainfall-runoff process. Water Resour. Res., 31, 2517-2530.
26. JAIN S. K., DAS D., and SRIVASTAVA D. K., 1999: Application of ANN for reservoir inflow prediction and operation. J. Water Resources Planning and Management, ASCE, 125, 263-271.
27. KISI O., 2005: Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish J. Eng. Env. Sci., 29, 9-20.
28. LIN J. Y., CHENG C. T. and CHAU K. W., 2006: Using support vector machines for long-term discharge prediction. Hydrological Sciences J., 51, 4, 599-612.
29. LIONG S. Y., LIM W. H., KOJIRI T., and HORI H., 2006: Advance flood forecasting for flood stricken Bangladesh with a fuzzy reasoning method. Hydrol. Process, 14, 431-448.
30. LIU H., SUN J., LIU L. and ZHANG H., 2009: Feature selection with dynamic mutual information. Pattern Recognition, 42, 7, 1330-1339.
31. MAHABIR C., HICKS F. E., and FAYEK A. R., 2000: Application of fuzzy logic to the seasonal runoff. Hydrol. Process, 17, 3749-3762.
32. MAIER H.R. AND DANDY G. C., 1998: Neural networks for the prediction and forecasting of water resources variables: A review of modeling issues and applications. Environmental Modeling and Software, 15, 101-123.
33. MAMDANI E. A., 1976: Advances in the linguistic synthesis of fuzzy controllers. Int. J. Man-Machine Studies, 8, 669-678.
34. MAMDANI E. A. and ASSILIAN S., 1975: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Studies, 7, 1-3.
35. MANNING, C. D., RAGHAVAN P. and SCHÜTZE H., 2008: Introduction to Information Retrieval. Cambridge University Press.
36. MARKUS M., 1997: Application of neural networks in streamflow forecasting. PhD thesis, Department of Civil Engineering. Colorado State University. Fort Collins. Colorado.
37. MAY R. J., DANDY G. C., MAIER H. R., and NIXON J. B., 2008a: Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environmental Modeling and Software, 23, 1289-1299.
38. MAY R. J., MAIER H. R., DANDY G. C., and FERNANDO T. G., 2008b: Non-linear variable selection for artificial neural networks using partial mutual information. Environmental Modeling and Software, 23, 1312-1326.
39. NAYAK P. C., SUDHEER K. P. and RAMASASTRI K. S., 2004a: Fuzzy computing based rainfall-runoff model for real time flood forecasting. Hydrol. Process, 17, 3749-3762.
40. PULIDO-CALVO I. and PORTELA, M. M., 2007: Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J. Hydrol., 332, 1-15.
41. SAAD M., BIGRAS P., TURGEON A., and DUQUETTE R., 1996: Fuzzy learning decomposition for the scheduling of hydroelectric power systems. Water Resour. Res., 32, 179-186.
42. SEN Z. and ALTUNKAYNAK A., 2006: A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrol. Process, 20, 1993-2009.
43. SHAMSELDIN A. Y., 1997: Application of a neural network technique to rainfall-runoff modeling. J. Hydrol., 199, 272-293.
44. SIVAKUMAR B., JAYAWARDENA A. W. and FERNANDO T. M. K. G., 2002: River flow forecasting: Use of phase space reconstruction and artificial neural networks approaches. J. Hydrol., 265, 225-245.
45. SMITH J., and ELI R. N., 1995: Neural network models of rainfall-runoff process. J. Water Resources Planning and Management, 499-508.
46. SMOLA A. J., SCHLKOPF B., 2004: A tutorial on support vector regression. Statistics and Computing, 14, 199-222.
47. SOROOSHIAN S., DAUN Q., and GUPTA V. K., 1993: Calibration of Rainfall-Runoff Models; Application of Global Optimization to the Sacramento Soil Moisture Accounting Model. Water Resour. Res., 29, 1185-1193.
48. SUGENO M,. and YASUKAWA T., 1993: A fuzzy-logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems, 1, 7-31.
49. TAHERI SHAHRAIYNI H., 2007: Extraction of water quality parameters using the satellite data and inverse modeling of radiative transfer equation by Active Learning Method. PhD Thesis, School of Civil Engineering, Sharif University of Technology, Iran.
50. TAHERI SHAHRAIYNI H., BAGHERI SHOURAKI S., FELL F., SCHAALE M., FISCHER J., TAVAKOLI A., PREUSKER R., TAJRISHY M., VATANDOUST M., and KHODAPARAST H., 2009: Application of the active learning method to the retrieval of pigment from spectral remote sensing reflectance data. Int. J Remote Sensing, 30, 1045-1065.
51. TAHERI SHAHRAIYNI H., 2010: Fuzzy modeling by active learning method. Advanced Technologies, 1st Edn., In-Tech Publisher.
52. TAKAGI T., and SUGENO M., 1985: Fuzzy identification of systems and its applications to modeling and control., IEEE Transactions on Systems. Man. and Cybernetics, SMC15, 116-132.
53. TAN P. N., STEINBACH M. and KUMAR V., 2006: Introduction to data mining. Addison Wesley.
54. TOKAR A. S. and JOHNSON P. A., 1999: Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng., ASCE, 4, 232-239.
55. TAYFUR, G. and SINGH, V.P., 2006: ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff. J. Hydraul. Engng, 132, 12, 1321-1330.
56. US ARMY CORPS of ENGINEERS (USACE)., 2000: HECHMS Technical Reference Manual. Davis, C.A, http://www.hec.usace.army.mil/.
57. VAPNIK V. N., CORTES C., 1995: Support vector networks. Machine Learning, 20, 273-297.
58. VAPNIK V. N., 1998: Statistical Learning Theory. Wiley, New York.
59. WANG W., 2006: Stochasticity, nonlinearity and forecasting of streamflow processes. PhD Thesis, Hohai University.
60. WANG W. C., CHAU K. W., CHENG C. T. and QIU L., 2009: A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J. Hydrol., 374, 3-4, 294-306.
61. WU C.L., CHAU K. W. and LI Y. S., 2009: Predicting monthly streamflow using data-driven models coupled with datapreprocessing techniques. Water Resour. Res., 45, W08432, doi:10.1029/2007WR006737.
62. YAPO P., GUPTA V. K., and SOROOSHIAN S., 1996: Calibration of Conceptual Rainfall-Runoff Models: Sensitivity to Calibration Data. J. Hydrol., 181, 23-48.
63. YU P.S., CHEN S.T., and CHANG I.F., 2006: Support vector regression for real-time flood stage forecasting. J. Hydrol., 328, 704-716.
64. ZADEH L. A., 1965: Fuzzy sets. Information and Control. 338-353.
65. ZEALAND C. M., BURN D. H., and SIMONOVIC S. P., 1999: Short-term streamflow forecasting using artificial neural networks. J. Hydrol., 214, 32-48.
66. ZOUNEMAT-KERMANI M., TESHNEHLAB M., 2007: Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl. Soft Comput. J., doi:10.1016/j.asoc.2007.07.011.