Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sattar, Muhammad Nouman | - |
dc.contributor.author | Jehanzaib, Muhammad | - |
dc.contributor.author | Kim, Ji Eun | - |
dc.contributor.author | Kwon, Hyun-Han | - |
dc.contributor.author | Kim, Tae-Woong | - |
dc.date.accessioned | 2021-06-22T06:00:19Z | - |
dc.date.available | 2021-06-22T06:00:19Z | - |
dc.date.issued | 2020-09 | - |
dc.identifier.issn | 2073-4433 | - |
dc.identifier.issn | 2073-4433 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/923 | - |
dc.description.abstract | Drought is one of the most destructive natural hazards and results in negative effects on the environment, agriculture, economics, and society. A meteorological drought originates from atmospheric components, while a hydrological drought is influenced by properties of the hydrological cycle and generally induced by a continuous meteorological drought. Several studies have attempted to explain the cross dependencies between meteorological and hydrological droughts. However, these previous studies did not consider the propagation of drought classes. Therefore, in this study, to consider the drought propagation concept and to probabilistically assess the meteorological and hydrological drought classes, characterized by the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI), respectively, we employed the Markov Bayesian Classifier (MBC) model that combines the procedure of iteration of feature extraction, classification, and application for assessment of drought classes for both SPI and SRI. The classification results were compared using the observed SPI and SRI, as well as with previous findings, which demonstrated that the MBC was able to reasonably determine drought classes. The accuracy of the MBC model in predicting all the classes of meteorological drought varies from 36 to 76% and in predicting all the classes of hydrological drought varies from 33 to 70%. The advantage of the MBC-based classification is that it considers drought propagation, which is very useful for planning, monitoring, and mitigation of hydrological drought in areas having problems related to hydrological data availability. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Application of the Hidden Markov Bayesian Classifier and Propagation Concept for Probabilistic Assessment of Meteorological and Hydrological Droughts in South Korea | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/atmos11091000 | - |
dc.identifier.scopusid | 2-s2.0-85093942866 | - |
dc.identifier.wosid | 000580076400001 | - |
dc.identifier.bibliographicCitation | ATMOSPHERE, v.11, no.9 | - |
dc.citation.title | ATMOSPHERE | - |
dc.citation.volume | 11 | - |
dc.citation.number | 9 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology | - |
dc.relation.journalResearchArea | Meteorology & Atmospheric Sciences | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences | - |
dc.relation.journalWebOfScienceCategory | Meteorology & Atmospheric Sciences | - |
dc.subject.keywordPlus | CLIMATE-CHANGE | - |
dc.subject.keywordPlus | RIVER-BASIN | - |
dc.subject.keywordPlus | AGRICULTURAL DROUGHT | - |
dc.subject.keywordPlus | IMPACT | - |
dc.subject.keywordPlus | CHALLENGES | - |
dc.subject.keywordAuthor | standardized precipitation index | - |
dc.subject.keywordAuthor | standardized runoff index | - |
dc.subject.keywordAuthor | drought classes | - |
dc.subject.keywordAuthor | propagation | - |
dc.subject.keywordAuthor | Markov Bayesian Classifier | - |
dc.identifier.url | https://www.mdpi.com/2073-4433/11/9/1000 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.