IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v33y2004i1p137-159.html
   My bibliography  Save this article

Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA

Author

Listed:
  • Tsegaye Tadesse
  • Donald Wilhite
  • Sherri Harms
  • Michael Hayes
  • Steve Goddard

Abstract

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought. Copyright Kluwer Academic Publishers 2004

Suggested Citation

  • Tsegaye Tadesse & Donald Wilhite & Sherri Harms & Michael Hayes & Steve Goddard, 2004. "Drought Monitoring Using Data Mining Techniques: A Case Study for Nebraska, USA," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 33(1), pages 137-159, September.
    • Handle: RePEc:spr:nathaz:v:33:y:2004:i:1:p:137-159
    DOI: 10.1023/B:NHAZ.0000035020.76733.0b
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1023/B:NHAZ.0000035020.76733.0b
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1023/B:NHAZ.0000035020.76733.0b?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lingkui Meng & Ting Dong & Wen Zhang, 2016. "Drought monitoring using an Integrated Drought Condition Index (IDCI) derived from multi-sensor remote sensing data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(2), pages 1135-1152, January.
    2. Watinee Thavorntam & Netnapid Tantemsapya & Leisa Armstrong, 2015. "A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(3), pages 1453-1474, July.
    3. Vahid Nourani & Mohammad Taghi Sattari & Amir Molajou, 2017. "Threshold-Based Hybrid Data Mining Method for Long-Term Maximum Precipitation Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(9), pages 2645-2658, July.
    4. Mohammad Taghi Sattari & Fatemeh Shaker Sureh & Ercan Kahya, 2020. "Monthly precipitation assessments in association with atmospheric circulation indices by using tree-based models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(3), pages 1077-1094, July.
    5. Lingkui Meng & Ting Dong & Wen Zhang, 2016. "Drought monitoring using an Integrated Drought Condition Index (IDCI) derived from multi-sensor remote sensing data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 80(2), pages 1135-1152, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:33:y:2004:i:1:p:137-159. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.