Proposing a new framework for analyzing the severity of meteorological drought

The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteo...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rizwan Niaz [verfasserIn] Mohammed M. A. Almazah [verfasserIn] A. Y. Al-Rezami [verfasserIn] Zulfiqar Ali [verfasserIn] Ijaz Hussain [verfasserIn] Talha Omer [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region

Übergeordnetes Werk:	In: Geocarto International - Taylor & Francis Group, 2023, 38(2023), 1
Übergeordnetes Werk:	volume:38 ; year:2023 ; number:1

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/10106049.2023.2197512

Katalog-ID:	DOAJ099485915

Internformat


LEADER	01000naa a22002652 4500
001	DOAJ099485915
003	DE-627
005	20240414035008.0
007	cr uuu---uuuuu
008	240414s2023 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1080/10106049.2023.2197512 \|2 doi
035			\|a (DE-627)DOAJ099485915
035			\|a (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
050		0	\|a GB3-5030
100	0		\|a Rizwan Niaz \|e verfasserin \|4 aut
245	1	0	\|a Proposing a new framework for analyzing the severity of meteorological drought
264		1	\|c 2023
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
650		4	\|a spatio-temporal
650		4	\|a standardized drought index
650		4	\|a steady-state probabilities
650		4	\|a monte carlo feature selection
650		4	\|a homogeneous region
653		0	\|a Physical geography
700	0		\|a Mohammed M. A. Almazah \|e verfasserin \|4 aut
700	0		\|a A. Y. Al-Rezami \|e verfasserin \|4 aut
700	0		\|a Zulfiqar Ali \|e verfasserin \|4 aut
700	0		\|a Ijaz Hussain \|e verfasserin \|4 aut
700	0		\|a Talha Omer \|e verfasserin \|4 aut
773	0	8	\|i In \|t Geocarto International \|d Taylor & Francis Group, 2023 \|g 38(2023), 1 \|w (DE-627)364462809 \|w (DE-600)2109550-4 \|x 17520762 \|7 nnns
773	1	8	\|g volume:38 \|g year:2023 \|g number:1
856	4	0	\|u https://doi.org/10.1080/10106049.2023.2197512 \|z kostenfrei
856	4	0	\|u https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c \|z kostenfrei
856	4	0	\|u http://dx.doi.org/10.1080/10106049.2023.2197512 \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1010-6049 \|y Journal toc \|z kostenfrei
856	4	2	\|u https://doaj.org/toc/1752-0762 \|y Journal toc \|z kostenfrei
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_DOAJ
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_213
912			\|a GBV_ILN_224
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2001
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2007
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2026
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4393
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 38 \|j 2023 \|e 1

Indexfelder

author_variant	r n rn m m a a mmaa a y a r ayar z a za i h ih t o to
matchkey_str	article:17520762:2023----::rpsnaefaeokoaayighsvrtomt
hierarchy_sort_str	2023
callnumber-subject-code	GB
publishDate	2023
allfields	10.1080/10106049.2023.2197512 doi (DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c DE-627 ger DE-627 rakwb eng GB3-5030 Rizwan Niaz verfasserin aut Proposing a new framework for analyzing the severity of meteorological drought 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography Mohammed M. A. Almazah verfasserin aut A. Y. Al-Rezami verfasserin aut Zulfiqar Ali verfasserin aut Ijaz Hussain verfasserin aut Talha Omer verfasserin aut In Geocarto International Taylor & Francis Group, 2023 38(2023), 1 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:38 year:2023 number:1 https://doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c kostenfrei http://dx.doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2023 1
spelling	10.1080/10106049.2023.2197512 doi (DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c DE-627 ger DE-627 rakwb eng GB3-5030 Rizwan Niaz verfasserin aut Proposing a new framework for analyzing the severity of meteorological drought 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography Mohammed M. A. Almazah verfasserin aut A. Y. Al-Rezami verfasserin aut Zulfiqar Ali verfasserin aut Ijaz Hussain verfasserin aut Talha Omer verfasserin aut In Geocarto International Taylor & Francis Group, 2023 38(2023), 1 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:38 year:2023 number:1 https://doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c kostenfrei http://dx.doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2023 1
allfields_unstemmed	10.1080/10106049.2023.2197512 doi (DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c DE-627 ger DE-627 rakwb eng GB3-5030 Rizwan Niaz verfasserin aut Proposing a new framework for analyzing the severity of meteorological drought 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography Mohammed M. A. Almazah verfasserin aut A. Y. Al-Rezami verfasserin aut Zulfiqar Ali verfasserin aut Ijaz Hussain verfasserin aut Talha Omer verfasserin aut In Geocarto International Taylor & Francis Group, 2023 38(2023), 1 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:38 year:2023 number:1 https://doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c kostenfrei http://dx.doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2023 1
allfieldsGer	10.1080/10106049.2023.2197512 doi (DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c DE-627 ger DE-627 rakwb eng GB3-5030 Rizwan Niaz verfasserin aut Proposing a new framework for analyzing the severity of meteorological drought 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography Mohammed M. A. Almazah verfasserin aut A. Y. Al-Rezami verfasserin aut Zulfiqar Ali verfasserin aut Ijaz Hussain verfasserin aut Talha Omer verfasserin aut In Geocarto International Taylor & Francis Group, 2023 38(2023), 1 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:38 year:2023 number:1 https://doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c kostenfrei http://dx.doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2023 1
allfieldsSound	10.1080/10106049.2023.2197512 doi (DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c DE-627 ger DE-627 rakwb eng GB3-5030 Rizwan Niaz verfasserin aut Proposing a new framework for analyzing the severity of meteorological drought 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography Mohammed M. A. Almazah verfasserin aut A. Y. Al-Rezami verfasserin aut Zulfiqar Ali verfasserin aut Ijaz Hussain verfasserin aut Talha Omer verfasserin aut In Geocarto International Taylor & Francis Group, 2023 38(2023), 1 (DE-627)364462809 (DE-600)2109550-4 17520762 nnns volume:38 year:2023 number:1 https://doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c kostenfrei http://dx.doi.org/10.1080/10106049.2023.2197512 kostenfrei https://doaj.org/toc/1010-6049 Journal toc kostenfrei https://doaj.org/toc/1752-0762 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 38 2023 1
language	English
source	In Geocarto International 38(2023), 1 volume:38 year:2023 number:1
sourceStr	In Geocarto International 38(2023), 1 volume:38 year:2023 number:1
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region Physical geography
isfreeaccess_bool	true
container_title	Geocarto International
authorswithroles_txt_mv	Rizwan Niaz @@aut@@ Mohammed M. A. Almazah @@aut@@ A. Y. Al-Rezami @@aut@@ Zulfiqar Ali @@aut@@ Ijaz Hussain @@aut@@ Talha Omer @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	364462809
id	DOAJ099485915
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ099485915</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414035008.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240414s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/10106049.2023.2197512</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ099485915</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GB3-5030</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Rizwan Niaz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Proposing a new framework for analyzing the severity of meteorological drought</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatio-temporal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">standardized drought index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">steady-state probabilities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">monte carlo feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">homogeneous region</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physical geography</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed M. A. Almazah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">A. Y. Al-Rezami</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zulfiqar Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ijaz Hussain</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Talha Omer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Geocarto International</subfield><subfield code="d">Taylor & Francis Group, 2023</subfield><subfield code="g">38(2023), 1</subfield><subfield code="w">(DE-627)364462809</subfield><subfield code="w">(DE-600)2109550-4</subfield><subfield code="x">17520762</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:38</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/10106049.2023.2197512</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1080/10106049.2023.2197512</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1010-6049</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1752-0762</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">38</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield></datafield></record></collection>
callnumber-first	G - Geography, Anthropology, Recreation
author	Rizwan Niaz
spellingShingle	Rizwan Niaz misc GB3-5030 misc spatio-temporal misc standardized drought index misc steady-state probabilities misc monte carlo feature selection misc homogeneous region misc Physical geography Proposing a new framework for analyzing the severity of meteorological drought
authorStr	Rizwan Niaz
ppnlink_with_tag_str_mv	@@773@@(DE-627)364462809
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut aut
collection	DOAJ
remote_str	true
callnumber-label	GB3-5030
illustrated	Not Illustrated
issn	17520762
topic_title	GB3-5030 Proposing a new framework for analyzing the severity of meteorological drought spatio-temporal standardized drought index steady-state probabilities monte carlo feature selection homogeneous region
topic	misc GB3-5030 misc spatio-temporal misc standardized drought index misc steady-state probabilities misc monte carlo feature selection misc homogeneous region misc Physical geography
topic_unstemmed	misc GB3-5030 misc spatio-temporal misc standardized drought index misc steady-state probabilities misc monte carlo feature selection misc homogeneous region misc Physical geography
topic_browse	misc GB3-5030 misc spatio-temporal misc standardized drought index misc steady-state probabilities misc monte carlo feature selection misc homogeneous region misc Physical geography
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Geocarto International
hierarchy_parent_id	364462809
hierarchy_top_title	Geocarto International
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)364462809 (DE-600)2109550-4
title	Proposing a new framework for analyzing the severity of meteorological drought
ctrlnum	(DE-627)DOAJ099485915 (DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c
title_full	Proposing a new framework for analyzing the severity of meteorological drought
author_sort	Rizwan Niaz
journal	Geocarto International
journalStr	Geocarto International
callnumber-first-code	G
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2023
contenttype_str_mv	txt
author_browse	Rizwan Niaz Mohammed M. A. Almazah A. Y. Al-Rezami Zulfiqar Ali Ijaz Hussain Talha Omer
container_volume	38
class	GB3-5030
format_se	Elektronische Aufsätze
author-letter	Rizwan Niaz
doi_str_mv	10.1080/10106049.2023.2197512
author2-role	verfasserin
title_sort	proposing a new framework for analyzing the severity of meteorological drought
callnumber	GB3-5030
title_auth	Proposing a new framework for analyzing the severity of meteorological drought
abstract	The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
abstractGer	The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
abstract_unstemmed	The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2026 GBV_ILN_2034 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700
container_issue	1
title_short	Proposing a new framework for analyzing the severity of meteorological drought
url	https://doi.org/10.1080/10106049.2023.2197512 https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c http://dx.doi.org/10.1080/10106049.2023.2197512 https://doaj.org/toc/1010-6049 https://doaj.org/toc/1752-0762
remote_bool	true
author2	Mohammed M. A. Almazah A. Y. Al-Rezami Zulfiqar Ali Ijaz Hussain Talha Omer
author2Str	Mohammed M. A. Almazah A. Y. Al-Rezami Zulfiqar Ali Ijaz Hussain Talha Omer
ppnlink	364462809
callnumber-subject	GB - Physical Geography
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1080/10106049.2023.2197512
callnumber-a	GB3-5030
up_date	2024-07-03T23:01:11.975Z
_version_	1803600707689906176
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">DOAJ099485915</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240414035008.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240414s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/10106049.2023.2197512</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)DOAJ099485915</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)DOAJ6fc851b007a44e3dbcb2a3242db8b09c</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">GB3-5030</subfield></datafield><datafield tag="100" ind1="0" ind2=" "><subfield code="a">Rizwan Niaz</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Proposing a new framework for analyzing the severity of meteorological drought</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">spatio-temporal</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">standardized drought index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">steady-state probabilities</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">monte carlo feature selection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">homogeneous region</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Physical geography</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Mohammed M. A. Almazah</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">A. Y. Al-Rezami</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Zulfiqar Ali</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Ijaz Hussain</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Talha Omer</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Geocarto International</subfield><subfield code="d">Taylor & Francis Group, 2023</subfield><subfield code="g">38(2023), 1</subfield><subfield code="w">(DE-627)364462809</subfield><subfield code="w">(DE-600)2109550-4</subfield><subfield code="x">17520762</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:38</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1080/10106049.2023.2197512</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doaj.org/article/6fc851b007a44e3dbcb2a3242db8b09c</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://dx.doi.org/10.1080/10106049.2023.2197512</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1010-6049</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">https://doaj.org/toc/1752-0762</subfield><subfield code="y">Journal toc</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_DOAJ</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2001</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2007</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2026</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">38</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield></datafield></record></collection>
score	7.401038

Nicht das Richtige dabei?

Schreiben Sie uns!

Proposing a new framework for analyzing the severity of meteorological drought

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?