Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions
AbstractThe objective of this study was to develop information mining methodology for drought modeling and predictions using historical records of climate, satellite, environmental, and oceanic data. The classification and regression tree (CART) approach was used for extracting drought episodes at different time-lag prediction intervals. Using the CART approach, a number of successful model trees were constructed, which can easily be interpreted and used by decision makers in their drought management decisions. The regression rules produced by CART were found to have correlation coefficients from 0.71–0.95 in rules-alone modeling. The accuracies of the models were found to be higher in the instance and rules model (0.77–0.96) compared to the rules-alone model. From the experimental analysis, it was concluded that different combinations of the nearest neighbor and committee models significantly increase the performances of CART drought models. For more robust results from the developed methodology, it is recommended that future research focus on selecting relevant attributes for slow-onset drought episode identification and prediction. View Full-Text
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Demisse, G.B.; Tadesse, T.; Atnafu, S.; Hill, S.; Wardlow, B.D.; Bayissa, Y.; Shiferaw, A. Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions. Information 2017, 8, 79.
Demisse GB, Tadesse T, Atnafu S, Hill S, Wardlow BD, Bayissa Y, Shiferaw A. Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions. Information. 2017; 8(3):79.Chicago/Turabian Style
Demisse, Getachew B.; Tadesse, Tsegaye; Atnafu, Solomon; Hill, Shawndra; Wardlow, Brian D.; Bayissa, Yared; Shiferaw, Andualem. 2017. "Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions." Information 8, no. 3: 79.
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