Abstract
Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. However, improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. This emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Therefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. The MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). In MBCSTCS, the estimated mixing proportions and the posterior probabilities are used to compute probability distribution associated with the future steps of transitions. Furthermore, MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. The MBCSTCS is applied to the six meteorological stations in the northern area of Pakistan. Moreover, it is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. These findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought.
Highlights
Drought is relatively more volatile than other natural disasters, and traditional valuations or forecast procedures are failed to predict it
Several studies related to model-based clustering are available in the literature; it has not yet received greater attention in drought analysis. erefore, this study developed a new technique known as Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS) to precisely predict drought occurrences for spatiotemporal categorical sequences. e performance of the proposed technique is assessed by using six meteorological stations in the northern area of Pakistan
Ese observed drought classes are further used to find the probability distribution associated with the three-step transition from the last state in the various sequences. e posterior vector related to these sequences specifies the parameter values. e obtained results show that the most likely state to visit in three steps is Normal dry (ND), which means the probability associated with ND is higher than the other selected states in varying sequences (Table 4)
Summary
Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences. Improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. Is emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Erefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. E MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. It is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. It is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. ese findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought
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