A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes

TL;DR

This paper introduces a novel weighting scheme for Weighted Markov Chain models to enhance drought prediction accuracy by effectively handling temporal drought classification data.

Contribution

It proposes a standardized weighting scheme based on normalized squared weighted Cohen Kappa for ordinal drought data in WMC models, improving drought prediction.

Findings

The scheme effectively captures changes in drought classifications.

Experimental results demonstrate improved prediction accuracy.

The method is flexible for real-world hydrological data.

Abstract

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.