  Drought is a complex stochastic natural hazard caused by prolonged shortage
of rainfall. Several environmental factors are involved in determining drought
classes at the specific monitoring station. Therefore, efficient sequence
processing techniques are required to explore and predict the periodic
information about the various episodes of drought classes. In this study, we
proposed a new weighting scheme to predict the probability of various drought
classes under Weighted Markov Chain (WMC) model. We provide a standardized
scheme of weights for ordinal sequences of drought classifications by
normalizing squared weighted Cohen Kappa. Illustrations of the proposed scheme
are given by including temporal ordinal data on drought classes determined by
the standardized precipitation temperature index (SPTI). Experimental results
show that the proposed weighting scheme for WMC model is sufficiently flexible
to address actual changes in drought classifications by restructuring the
transient behavior of a Markov chain. In summary, this paper proposes a new
weighting scheme to improve the accuracy of the WMC, specifically in the field
of hydrology.
