A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes
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...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2018/8954656 |
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| author | Zulfiqar Ali Ijaz Hussain Muhammad Faisal Ibrahim M. Almanjahie Muhammad Ismail Maqsood Ahmad Ishfaq Ahmad |
| author_facet | Zulfiqar Ali Ijaz Hussain Muhammad Faisal Ibrahim M. Almanjahie Muhammad Ismail Maqsood Ahmad Ishfaq Ahmad |
| author_sort | Zulfiqar Ali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5b266bf81d1a4b9d825c290bc7611298 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-5b266bf81d1a4b9d825c290bc76112982025-02-03T06:08:34ZengWileyAdvances in Meteorology1687-93091687-93172018-01-01201810.1155/2018/89546568954656A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought EpisodesZulfiqar Ali0Ijaz Hussain1Muhammad Faisal2Ibrahim M. Almanjahie3Muhammad Ismail4Maqsood Ahmad5Ishfaq Ahmad6Department of Statistics, Quaid-i-Azam University, Islamabad, PakistanDepartment of Statistics, Quaid-i-Azam University, Islamabad, PakistanFaculty of Health Studies, University of Bradford, BD7 1DP Bradford, UKDepartment of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi ArabiaDepartment of Statistics, COMSATS University Islamabad, Lahore Campus, PakistanDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, PakistanDepartment of Mathematics, College of Science, King Khalid University, Abha 61413, Saudi ArabiaDrought 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.http://dx.doi.org/10.1155/2018/8954656 |
| spellingShingle | Zulfiqar Ali Ijaz Hussain Muhammad Faisal Ibrahim M. Almanjahie Muhammad Ismail Maqsood Ahmad Ishfaq Ahmad A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes Advances in Meteorology |
| title | A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes |
| title_full | A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes |
| title_fullStr | A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes |
| title_full_unstemmed | A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes |
| title_short | A New Weighting Scheme in Weighted Markov Model for Predicting the Probability of Drought Episodes |
| title_sort | new weighting scheme in weighted markov model for predicting the probability of drought episodes |
| url | http://dx.doi.org/10.1155/2018/8954656 |
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