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A Machine Learning Approach for Probabilistic Drought Classification

A Machine Learning Approach for Probabilistic Drought Classification

This video was recorded at NASA Conference on Intelligent Data Understanding (CIDU) 2011, Mountain View, CA. Current methods of drought assessment utilize drought indices, such as the standardized precipitation index and Palmer drought severity index, that rely on subjective thresholds and hence cannot be universally applied across different climatic regions. In addition, most of the existing drought indices are not amenable to probabilistic treatment which is essential for quantifying model uncertainties in drought classification. This study applies a machine learning tool, the hidden Markov model (HMM), for probabilistic drought classification. The HMM-based drought index (HMM-DI) developed in this study, does not require specification of subjective thresholds and model parameters are... Show More


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