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Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling Cover

Comparison Between Active Learning Method and Support Vector Machine for Runoff Modeling

Journal of Hydrology and Hydromechanics
Volume 60 (2012): Issue 1 (March 2012)
By: ,  ,  ,   and    
Open Access
|Mar 2012
References

References

  1. ABBASS H. A., SARKER R. A., and NEWTON C. S., 2002: Data mining: a heuristic approach. Idea Group Publishing.
    Open DOISearch in Google Scholar
  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.
    Search in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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. 91-94.
    Search in Google Scholar
  5. BAGHERI SHOURAKI S., and HONDA N., 1999: Recursive fuzzy modeling based on fuzzy interpolation. J. Advanced Computational Intelligence, 3, 114-125.
    Open DOISearch in Google Scholar
  6. BATTITI R., 1994: Using mutual information for selecting features in supervised neural net learning. IEEE Transactions on Neural Networks,, 4, 537-550.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.,, 2, 1010, doi: 10.1029/2001WR000266. (http://www.agu.org/pubs/crossref/2002/2001WR000266.shtml)
    Search in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  12. CHAMBERS J. M., CLEVELAND W. S., KLEINER B., TUKEY P. A., 1983: Graphical methods for data analysis. Wadworth, Belmont, California, pp. 395.
    Search in Google Scholar
  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
    Open DOISearch in Google Scholar
  14. CHANG F. J. and CHANG Y. T., 2006: Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Res,, 1-10.
    Open DOISearch in Google Scholar
  15. CHANG F. J. and CHEN Y. C., 2001: Counter propagation fuzzy-neural network modeling approach to real time streamflow prediction. J. Hydrol.,, 153-164.
    Open DOISearch in Google Scholar
  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,, 6, 485-491.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.,, 3, 208-216.
    Open DOISearch in Google Scholar
  20. DUAN D., FERMINI B., and NATTELI S., 1992: Sustained Outward Current Observed after Ito1 Inactivation in Rabbit Atrial Myocytes Is a Novel Clcurrent'. Am. J. Physiol.,, H1967-1971.
    Open DOISearch in Google Scholar
  21. FAUSSET L., 1994: Fundamentals of neural networks: Architectures, algorithms and applications. Prentice Hall, 461p.
    Search in Google Scholar
  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.,, 3-4, 165-176.
    Open DOISearch in Google Scholar
  23. FIRAT M., and GUNGOR M., 2006: River flow estimation using adaptive neuro fuzzy inference system. Math. Compute. Simulate.,, 3-4, 87-96.
    Open DOISearch in Google Scholar
  24. FIRAT M., 2008: Comparison of artificial intelligence techniques for river flow forecasting. Hydrol. Earth Syst. Sci.,, 123-139.
    Open DOISearch in Google Scholar
  25. HSU K., GUPTA H. V., and SOROOSHIAN S., 1995: Artificial neural network modeling of rainfall-runoff process. Water Resour. Res.,, 2517-2530.
    Open DOISearch in Google Scholar
  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,, 263-271.
    Open DOISearch in Google Scholar
  27. KISI O., 2005: Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish J. Eng. Env. Sci.,, 9-20.
    Search in Google Scholar
  28. LIN J. Y., CHENG C. T. and CHAU K. W., 2006: Using support vector machines for long-term discharge prediction. Hydrological Sciences J.,, 4, 599-612.
    Open DOISearch in Google Scholar
  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,, 431-448.
    Open DOISearch in Google Scholar
  30. LIU H., SUN J., LIU L. and ZHANG H., 2009: Feature selection with dynamic mutual information. Pattern Recognition,, 7, 1330-1339.
    Open DOISearch in Google Scholar
  31. MAHABIR C., HICKS F. E., and FAYEK A. R., 2000: Application of fuzzy logic to the seasonal runoff. Hydrol. Process,, 3749-3762.
    Open DOISearch in Google Scholar
  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,, 101-124.
    Open DOISearch in Google Scholar
  33. MAMDANI E. A., 1976: Advances in the linguistic synthesis of fuzzy controllers. Int. J. Man-Machine Studies,, 669-678.
    Open DOISearch in Google Scholar
  34. MAMDANI E. A. and ASSILIAN S., 1975: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Machine Studies,, 1-3.
    Open DOISearch in Google Scholar
  35. MANNING, C. D., RAGHAVAN P. and SCHÜTZE H., 2008: Introduction to Information Retrieval. Cambridge University Press.
    Open DOISearch in Google Scholar
  36. MARKUS M., 1997: Application of neural networks in streamflow forecasting. PhD thesis, Department of Civil Engineering. Colorado State University. Fort Collins. Colorado.
    Search in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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,, 1312-1326.
    Open DOISearch in Google Scholar
  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,, 3749-3762.
    Search in Google Scholar
  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.
    Open DOISearch in Google Scholar
  41. SAAD M., BIGRAS P., TURGEON A., and DUQUETTE R., 1996: Fuzzy learning decomposition for the scheduling of hydroelectric power systems. Water Resour. Res.,, 179-186.
    Open DOISearch in Google Scholar
  42. SEN Z. and ALTUNKAYNAK A., 2006: A comparative fuzzy logic approach to runoff coefficient and runoff estimation. Hydrol. Process,, 1993-2009.
    Open DOISearch in Google Scholar
  43. SHAMSELDIN A. Y., 1997: Application of a neural network technique to rainfall-runoff modeling. J. Hydrol.,, 272-294.
    Open DOISearch in Google Scholar
  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.,, 225-245.
    Open DOISearch in Google Scholar
  45. SMITH J., and ELI R. N., 1995: Neural network models of rainfall-runoff process. J. Water Resources Planning and Management, 499-508.
    Open DOISearch in Google Scholar
  46. SMOLA A. J., SCHLKOPF B., 2004: A tutorial on support vector regression. Statistics and Computing,, 199-222.
    Open DOISearch in Google Scholar
  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.,, 1185-1194.
    Open DOISearch in Google Scholar
  48. SUGENO M,. and YASUKAWA T., 1993: A fuzzy-logic based approach to qualitative modeling. IEEE Transactions on Fuzzy Systems,, 7-31.
    Open DOISearch in Google Scholar
  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.
    Search in Google Scholar
  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,, 1045-1065.
    Open DOISearch in Google Scholar
  51. TAHERI SHAHRAIYNI H., 2010: Fuzzy modeling by active learning method. Advanced Technologies, 1st Edn., In-Tech Publisher.
    Open DOISearch in Google Scholar
  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,, 116-132.
    Open DOISearch in Google Scholar
  53. TAN P. N., STEINBACH M. and KUMAR V., 2006: Introduction to data mining. Addison Wesley.
    Search in Google Scholar
  54. TOKAR A. S. and JOHNSON P. A., 1999: Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng., ASCE,, 232-239.
    Open DOISearch in Google Scholar
  55. TAYFUR, G. and SINGH, V. P., 2006: ANN and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff. J. Hydraul. Engng,, 12, 1321-1330.
    Open DOISearch in Google Scholar
  56. US ARMY CORPS of ENGINEERS (USACE)., 2000: HECHMS Technical Reference Manual. Davis, C. A
    Search in Google Scholar
  57. VAPNIK V. N., CORTES C., 1995: Support vector networks. Machine Learning,, 273-297.
    Open DOISearch in Google Scholar
  58. VAPNIK V. N., 1998: Statistical Learning Theory. Wiley, New York.
    Search in Google Scholar
  59. WANG W., 2006: Stochasticity, nonlinearity and forecasting of streamflow processes. PhD Thesis, Hohai University.
    Search in Google Scholar
  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.,, 3-4, 294-306.
    Open DOISearch in Google Scholar
  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.,, W08432, doi:10.1029/2007WR006737.
    Open DOISearch in Google Scholar
  62. YAPO P., GUPTA V. K., and SOROOSHIAN S., 1996: Calibration of Conceptual Rainfall-Runoff Models: Sensitivity to Calibration Data. J. Hydrol.,, 23-48.
    Open DOISearch in Google Scholar
  63. YU P. S., CHEN S. T., and CHANG I. F., 2006: Support vector regression for real-time flood stage forecasting. J. Hydrol.,, 704-716.
    Open DOISearch in Google Scholar
  64. ZADEH L. A., 1965: Fuzzy sets. Information and Control. 338-353.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
  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.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10098-012-0002-7 | Journal eISSN: 1338-4333 (formerly 0042-790X) | Journal ISSN: 0042-790X
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Language: English, Slovak
Page range: 16 - 32
Published on: Mar 6, 2012
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Keywords:
Runoff Modeling,
Active Learning Method (ALM),
Support Vector Machine (SVM),
Fuzzy Modeling,
Genetic Algorithm,
Karoon River Basin
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2012 Hamid Shahraiyni, Mohammad Ghafouri, Saeed Shouraki, Bahram Saghafian, Mohsen Nasseri, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons License.

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