Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.
Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.
Identifying drought and its associations with climatic and oceanic parameters using data mining techniques
Abstract
Drought is a natural hazard that involves many climatological and environmental parameters with complex relationships. Improving our understanding of these relationships is necessary to reduce the impacts of drought. The main objective of this study is to improve drought monitoring by finding associations between drought and several oceanic and climatic parameters that could help users make knowledgeable decisions about drought response. This includes extracting useful information from the large historical data archives. Data mining is a new technology that can be used to interact with large databases and assist in solving problems related to drought by extracting information from massive data archives. Data mining techniques include time-series algorithms that enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. The data mining techniques have been used for commercial application, medical research, and telecommunications, but never for drought. In this study, two new data mining algorithms [i.e., Representative Episodal Association Rule (REAR) and Minimal Occurrences With Constraints and Time Lags (MOWCATL)] have been developed to identify the relationships between oceanic parameters and drought indices. The algorithms can identify drought episodes separate from normal and wet conditions to find target-specific relationships with oceanic parameters instead of the traditional global associations. The algorithms provide flexibility in time-series analyses, allowing the discovery of relationships of the parameters with time lags using sliding windows. Moreover, these algorithms allow for the analysis of large amount of data and complicated computations to be executed within reasonable amounts of time. Using both algorithms, the analyses of the rules generated for selected stations and state-averaged data of Nebraska from 1950 to 1999 indicate that most occurrences of droughts are associated with positive values of Southern Oscillation Index (SOI), negative values of Multivariate ENSO Index (MEI), and negative values of Pacific Decadal Oscillation (PDO). This strong relationship between oceanic indices and droughts indicate that the oceanic parameters could be used as precursors of drought.
Subject Area
Environmental science|Geophysics
Recommended Citation
Tadesse, Tsegaye, "Identifying drought and its associations with climatic and oceanic parameters using data mining techniques" (2002). ETD collection for University of Nebraska-Lincoln. AAI3059971.
https://digitalcommons.unl.edu/dissertations/AAI3059971