Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions

Authors

Getachew B. Demisse, University of Nebraska - LincolnFollow
Tsegaye Tadesse, University of Nebraska - LincolnFollow
Solomon Atnafu, Addis Ababa UniversityFollow
Shawndra Hill, University of PennsylvaniaFollow
Brian D. Wardlow, University of Nebraska - LincolnFollow
Yared Bayissa, University of Nebraska - LincolnFollow
Andualem Shiferaw, University of Nebraska - LincolnFollow

Date of this Version

2017

Citation

Information 2017, 8, 79; doi:10.3390/info8030079

Comments

Copyright © 2017, the authors. Licensee MDPI, Basel, Switzerland. Open access, Creative Commons Attribution license 4.0.

Abstract

The 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.