A Machine Learning Approach for Probabilistic Drought Classification
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 determined from historical data during parameter estimation. The drought classifications obtained using HMM-DI are compared with SPI results. The HMM-DI reveals new insights into the frequency and severity of droughts and their spatio-temporal variations. The effectiveness of HMM-DI is assessed by its application to monthly precipitation data over India. The results suggest that HMM-DI can be a promising alternative to conventional drought indices.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !