Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas
Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of...
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| Veröffentlicht in: | Environmental modeling & assessment 2021-10, Vol.26 (5), p.803-821 |
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| creator | Bahmani, Shadi Naganna, Sujay Raghavendra Ghorbani, Mohammad Ali Shahabi, Mahmood Asadi, Esmaeil Shahid, Shamsuddin |
| description | Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of Iran based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Rainfall and evapotranspiration data from 47 synoptic stations over 20 years were used to generate SPI and SPEI, and topographical (altitude) data were used for GWRKrig. The results obtained using GWRKrig were compared with that of standalone GWR, regression kriging (RegKrig), and ordinary kriging (Krig) methods. The GWRKrig method emerged as a promising tool for spatial interpolation of drought indices based on performance evaluation measures, namely, the root mean squared error (RMSE) and coefficient of determination (
R
2
). The SPEI-based drought intensity interpolated via GWRKrig revealed relatively precise spatial variability of drought zones. The method proposed in this study would assist in the accurate delineation of drought-prone areas, which is the foremost venture in the planning hierarchy of drought management schemes and their implementation. |
| doi_str_mv | 10.1007/s10666-021-09789-z |
| format | Article |
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R
2
). The SPEI-based drought intensity interpolated via GWRKrig revealed relatively precise spatial variability of drought zones. The method proposed in this study would assist in the accurate delineation of drought-prone areas, which is the foremost venture in the planning hierarchy of drought management schemes and their implementation.</description><identifier>ISSN: 1420-2026</identifier><identifier>EISSN: 1573-2967</identifier><identifier>DOI: 10.1007/s10666-021-09789-z</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Applications of Mathematics ; Comparative analysis ; Delineation ; Disaster management ; Drought ; Drought index ; Droughts ; Earth and Environmental Science ; Emergency preparedness ; Environment ; Evapotranspiration ; Math. Appl. in Environmental Science ; Mathematical Modeling and Industrial Mathematics ; Operations Research/Decision Theory ; Performance evaluation ; Precipitation ; Rain ; Rain and rainfall ; Rainfall ; Regression models ; Root-mean-square errors ; Standardized precipitation index ; State-of-the-art reviews</subject><ispartof>Environmental modeling & assessment, 2021-10, Vol.26 (5), p.803-821</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</rights><rights>COPYRIGHT 2021 Springer</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-a85e165c39d3a66f06e54600e225c6086d9415eb25fe16d2d2877877ee2fd3e03</citedby><cites>FETCH-LOGICAL-c358t-a85e165c39d3a66f06e54600e225c6086d9415eb25fe16d2d2877877ee2fd3e03</cites><orcidid>0000-0002-0482-1936</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10666-021-09789-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10666-021-09789-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Bahmani, Shadi</creatorcontrib><creatorcontrib>Naganna, Sujay Raghavendra</creatorcontrib><creatorcontrib>Ghorbani, Mohammad Ali</creatorcontrib><creatorcontrib>Shahabi, Mahmood</creatorcontrib><creatorcontrib>Asadi, Esmaeil</creatorcontrib><creatorcontrib>Shahid, Shamsuddin</creatorcontrib><title>Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas</title><title>Environmental modeling & assessment</title><addtitle>Environ Model Assess</addtitle><description>Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of Iran based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Rainfall and evapotranspiration data from 47 synoptic stations over 20 years were used to generate SPI and SPEI, and topographical (altitude) data were used for GWRKrig. The results obtained using GWRKrig were compared with that of standalone GWR, regression kriging (RegKrig), and ordinary kriging (Krig) methods. The GWRKrig method emerged as a promising tool for spatial interpolation of drought indices based on performance evaluation measures, namely, the root mean squared error (RMSE) and coefficient of determination (
R
2
). The SPEI-based drought intensity interpolated via GWRKrig revealed relatively precise spatial variability of drought zones. The method proposed in this study would assist in the accurate delineation of drought-prone areas, which is the foremost venture in the planning hierarchy of drought management schemes and their implementation.</description><subject>Applications of Mathematics</subject><subject>Comparative analysis</subject><subject>Delineation</subject><subject>Disaster management</subject><subject>Drought</subject><subject>Drought index</subject><subject>Droughts</subject><subject>Earth and Environmental Science</subject><subject>Emergency preparedness</subject><subject>Environment</subject><subject>Evapotranspiration</subject><subject>Math. 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Assess</stitle><date>2021-10-01</date><risdate>2021</risdate><volume>26</volume><issue>5</issue><spage>803</spage><epage>821</epage><pages>803-821</pages><issn>1420-2026</issn><eissn>1573-2967</eissn><abstract>Assessing spatial variability of drought-prone areas is important for disaster preparedness and impact management. This study applied state-of-the-art geographically weighted regression hybridized with kriging method (GWRKrig) to map the spatial variability of drought-prone areas in the northwest of Iran based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Rainfall and evapotranspiration data from 47 synoptic stations over 20 years were used to generate SPI and SPEI, and topographical (altitude) data were used for GWRKrig. The results obtained using GWRKrig were compared with that of standalone GWR, regression kriging (RegKrig), and ordinary kriging (Krig) methods. The GWRKrig method emerged as a promising tool for spatial interpolation of drought indices based on performance evaluation measures, namely, the root mean squared error (RMSE) and coefficient of determination (
R
2
). The SPEI-based drought intensity interpolated via GWRKrig revealed relatively precise spatial variability of drought zones. The method proposed in this study would assist in the accurate delineation of drought-prone areas, which is the foremost venture in the planning hierarchy of drought management schemes and their implementation.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10666-021-09789-z</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-0482-1936</orcidid></addata></record> |
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| subjects | Applications of Mathematics Comparative analysis Delineation Disaster management Drought Drought index Droughts Earth and Environmental Science Emergency preparedness Environment Evapotranspiration Math. Appl. in Environmental Science Mathematical Modeling and Industrial Mathematics Operations Research/Decision Theory Performance evaluation Precipitation Rain Rain and rainfall Rainfall Regression models Root-mean-square errors Standardized precipitation index State-of-the-art reviews |
| title | Geographically Weighted Regression Hybridized with Kriging Model for Delineation of Drought-Prone Areas |
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