Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences
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 dr...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/7145168 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849306420351074304 |
|---|---|
| author | Rizwan Niaz Mohammed M. A. Almazah Xiang Zhang Ijaz Hussain Muhammad Faisal |
| author_facet | Rizwan Niaz Mohammed M. A. Almazah Xiang Zhang Ijaz Hussain Muhammad Faisal |
| author_sort | Rizwan Niaz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-34d34d3b88ad42a988ff0b2bbc2b3085 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-34d34d3b88ad42a988ff0b2bbc2b30852025-08-20T03:55:06ZengWileyComplexity1099-05262021-01-01202110.1155/2021/7145168Prediction for Various Drought Classes Using Spatiotemporal Categorical SequencesRizwan Niaz0Mohammed M. A. Almazah1Xiang Zhang2Ijaz Hussain3Muhammad Faisal4Department of StatisticsDepartment of MathematicsNational Engineering Research Center of Geographic Information SystemDepartment of StatisticsFaculty of Health StudiesDrought 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.http://dx.doi.org/10.1155/2021/7145168 |
| spellingShingle | Rizwan Niaz Mohammed M. A. Almazah Xiang Zhang Ijaz Hussain Muhammad Faisal Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences Complexity |
| title | Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences |
| title_full | Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences |
| title_fullStr | Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences |
| title_full_unstemmed | Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences |
| title_short | Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences |
| title_sort | prediction for various drought classes using spatiotemporal categorical sequences |
| url | http://dx.doi.org/10.1155/2021/7145168 |
| work_keys_str_mv | AT rizwanniaz predictionforvariousdroughtclassesusingspatiotemporalcategoricalsequences AT mohammedmaalmazah predictionforvariousdroughtclassesusingspatiotemporalcategoricalsequences AT xiangzhang predictionforvariousdroughtclassesusingspatiotemporalcategoricalsequences AT ijazhussain predictionforvariousdroughtclassesusingspatiotemporalcategoricalsequences AT muhammadfaisal predictionforvariousdroughtclassesusingspatiotemporalcategoricalsequences |