SciELO - Scientific Electronic Library Online

SciELO - Scientific Electronic Library Online

Referencias del artículo

YUREKLI, K.; TAGHI SATTARI, M.; ANLI, A. S.  y  HINIS, M. A.. Seasonal and annual regional drought prediction by using data-mining approach. Atmósfera [online]. 2012, vol.25, n.1, pp. 85-105. ISSN 0187-6236.

    Agnew C. T., 1990. Spatial aspects of drought in the Sahel. J. Arid Environ. 18, 279-293. [ Links ]

    Agnew C. T. and A. Warren, 1996. A framework for tackling drought and degradation. J. Arid Environ. 33, 309-320. [ Links ]

    Alatise M. O. and O. B. Ikumawoyi, 2007. Evaluation of drought from rainfall data for lokoja. A confluence of two major rivers. Electronic Journal of Polish Agricultural Universities Tomo 10, 1, Art. 5, Ondo State, Nigeria Available Online: http://www.ejpau.media.pl/volume10/issue1/art-05.html. Accessed in February 2010. [ Links ]

    Alley W. M., 1984. The palmer drought severity index: limits and assumptions. J. Clim. App. Clim. Meteorol. 23, 1100-1109. [ Links ]

    Anonymous, 1970. Soils of Yeşilirmak Basin. General Directorate of Soil and Water Publications, Ankara. [ Links ]

    Belda F. and M. C. Penadés, 2007. Using data-mining techniques for monitoring climatic variations. Application to drought. 7th EMS Annual Meeting / 8th European Conference on Applications of Meteorology, San Lorenzo de El Escorial, Spain, 01-05 October. Available online: http://meetings.copernicus.org/www.cosis.net/abstracts/EMS2007/00290/EMS2007-J-00290.pdf. Accessed in : February 2010 [ Links ]

    Edwards D. C. and T. B. McKee, 1997. Characteristics of 20th century drought in the United States at multiple time scales. Climatology Report Number 97-2, Colorado State University, Fort Collins, CO. [ Links ]

    Fayyad U. M., G. Piatetsky-Shapiro and P. Smyth, 1996. From data mining to knowledge discovery: An Overview. In: Advances in knowledge discovery and data mining (U. M. Fayyad, Ed.). AAAI Press and MIT Press, USA, pp 1-34. [ Links ]

    Florian T. B., A. S. Dragan and A. W. Godfrey, 2003. Water reservoir control with data mining. J. Water Res. Manage. 129, 26-34. [ Links ]

    Greenwood J. A., J. M. Landwehr, N. C. Matalas and J. R. Wallis, 1979. Probability weighted moments: Definition and relation to parameters of several distributions expressible in inverse form. Water Resour. Res. 15, 1049-1054. [ Links ]

    Giddings L., Soto M., Rutherford and M. B.M., Maarouf, 2005. Standardized precipitation index zones for Mexico. Atmósfera 18, 33-56. [ Links ]

    Guttman N. B., 1998. Comparing the Palmer drought index and the standardized precipitation index. J. Am. Water Resour. As. 34, 113-121. [ Links ]

    Guttman N. B., 1999. Accepting the Standardized Precipitation Index: A Calculation algorithm. J. Am. Water Resour. As. 35, 311-322. [ Links ]

    Han J. and M. Kamber, 2006. Data mining: Concepts and techniques. Morgan Kaufmann Publishers, New York, 664 pp. [ Links ]

    Hosking J. R. M., 1990. L-Moments: Analysis and estimation of distributions using linear combinations of order statistics. J. Roy. Stat. Soc. B. 52, 105-124. [ Links ]

    Hosking J. R. M. and J. R. Wallis, 1993. Some statistics useful in regional frequency analysis. Water Resour. Res 29, 271-281. [ Links ]

    Hosking J. R. M., 1996. Fortran routines for use with the method of L-moments. Research Report RC 20525, Version 3, New York, USA, 33 pp. [ Links ]

    Hosking J. R. M. and J. R. T. Wallis, 1997. Regional frequency analysis: An approach based on L-moments. Cambridge University Press, 34 pp. [ Links ]

    Komuscu A. U., 1999. Using the SPI to analyze spatial and temporal patterns of drought in Turkey. Drought Network News 11, 7-13. [ Links ]

    Kumar M. N., C. S. Murthy, M. V. R. Sesha Sai and P. S. Roy, 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Meteorol. Appl. 16, 381-389. [ Links ]

    Labedzki L., 2007. Estimation of local drought frequency in central Poland using the standardized precipitation index SPI. Irrig. Drain. 56, 67-77. [ Links ]

    Lana X., C. Serra and A. Burguena, 2001. Patterns of monthly rainfall shortage and excess in terms of the standardized precipitation index for Catalonia (NE Spain). Int. J. Climatology. 21, 1669-1691. [ Links ]

    Le Houerou H. N., 1996. Climate change, drought and desertification. J. Arid Environments 34, 133-185. [ Links ]

    Loukas A. and L. Vasiliades, 2004. Probabilistic analysis of drought spatiotemporal characteristics in Thessaly region, Greece. Nat. Hazards Earth Syst. 4, 719-731. [ Links ]

    McKee T. B., N. J. Doesken and J. Kleist, 1993. The relationship of drought frequency and duration to time scales. 8th Conference on Applied Climatology, American Meteorological Society, Anaheim, California, 17-22 January, American Meteorological Society, Dallas, Texas, 15-20 January, Anaheim, CA. American Meteorological Society, Boston, MA, 179-184 [ Links ]

    McKee T. B., N. J. Doesken and J. Kleist, 1995. Drought monitoring with multiple time scales. Preprints, 9th American Meteorological Society, Dallas, TX, 233-236. [ Links ]

    Oladipo E. O., 1985. A comparative performance analysis of three meteorological drought indices. Int. J. Climatol. 5, 655-664. [ Links ]

    Palmer W. C., 1965. Meteorological drought. Research Paper No. 45, U.S. Weather Bureau, Washington, DC, 58 pp. [ Links ]

    Quinlan J. R., 1997. See5 (available from http://www.rulequest.com/see5-info.html) Accessed in February 2010. [ Links ]

    Redmond K. T., 2000. Integrated climate monitoring for drought detection. In Drought: A Global Assessment (Wilhite D. A., Ed.), Hazards and Disasters Series, Routledge, London, 145-158. [ Links ]

    Seiler R. A., M. Hayes and L. Bressan, 2002. Using the standardized precipitation index for flood risk monitoring. Int. J. Climatol. 22, 1365-1376. [ Links ]

    Sharma A., 2006. Spatial data mining for drought monitoring: An approach using temporal NDVI and rainfall relationship. Master thesis, The International Institute for Geo-information Science and Earth Observation, The Netherlands. [ Links ]

    Solomantine D. P. and K. N. Dulal, 2003. Model trees as an alternative to neural networks in rainfall-runoff modeling. Hydrolog. Sci. J. 48, 455-472. [ Links ]

    Sudha V., N. K. Ambujam and K. Venugopal, 2006. A data mining approach for deriving irrigation reservoir operating rules. Conference on Water Observation and Information System for Decision Support, Orhid, Macedonia. Available: http://balwois.com/balwois/administration/full_paper/ffp-http://balwois.com/balwois/administration/full_paper/ffp-643.pdf. Accessed in February 2010. [ Links ]

    Tadesse T., D. A. Wilhite S. K., Harms, M. J. Hayes and S. Goddard, 2004. Drought monitoring using data mining techniques: A case study for Nebraska, USA. Nat. Hazards 33, 137-159. [ Links ]

    Tallaksen L. M., H. Madsen and H. Hisdal, 2004. Frequency analysis. In: Hydrological drought. Processes and estimation methods for streamflow and groundwater (Tallaksen L. M., Van Lanen H. A. J. Eds.). Developments in water science, Elsevier Science B.V., Amsterdam, 199-271. [ Links ]

    Thorn H. C. S., 1966. Some methods of climatological analysis. WMO technics/note number No. 81, 16-22. [ Links ]

    Wu H., Hubbard, K. G. and D. A. Wilhite, 2004. An agrıcultural drought rısk-assessment model for corn and soybeans. Int. J. Climatol. 24, 723-741. [ Links ]

    Yamoah C. F., Walters D. T., Shapiro C. A., Francis C. A. AND M. J. Hayes, 2000. Standardized precipitation index and nitrogen rate effects on crop yields and risk distribution in maize. Agr. Ecosyst. Environ. 80, 113-120. [ Links ]