The Application of Data Mining for Drought Monitoring and Prediction
Tsegaye Tadesse (National Drought Mitigation Center, University of Nebraska, USA), Brian Wardlow (National Drought Mitigation Center, University of Nebraska, USA) and Michael J. Hayes (National Drought Mitigation Center, University of Nebraska, USA)
Copyright: © 2009
This chapter discusses the application of data mining to develop drought monitoring tools that enable monitoring and prediction of drought’s impact on vegetation conditions. These monitoring tools help decision makers to assess the current levels of drought-related vegetation stress and provide insight into the possible future trends in vegetation conditions at local and regional scales, which can be used to make knowledge-based decisions. The chapter summarizes current research using data mining approaches (e.g., association rules and decision-tree methods) to develop these types of drought monitoring tools and briefly explains how they are being integrated with decision support systems. Future direction in data mining techniques and drought research is also discussed. This chapter is intended to introduce how data mining is be used to enhance drought monitoring and prediction in the United States and assist others to understand how similar tools might be developed in other parts of the world.