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DOI	10.1007/s11069-022-05219-9
	Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms
	Orimoloye, Israel R.; Olusola, Adeyemi O.; Belle, Johanes A.; Pande, Chaitanya B.; Ololade, Olusola O.
通讯作者	Orimoloye, IR (corresponding author)，Univ Free State, Ctr Environm Management, Bloemfontein, South Africa. ; Orimoloye, IR (corresponding author)，Univ Free State, Disaster Management Training & Educ Ctr Africa, Bloemfontein, South Africa. ; Orimoloye, IR (corresponding author)，Univ Ft Hare, Dept Geog & Environm Sci, Private Bag X1314, ZA-5700 Alice, Eastern Cape Pr, South Africa.
来源期刊	NATURAL HAZARDS
ISSN	0921-030X
EISSN	1573-0840
出版年	2022-01
英文摘要	Droughts are particularly disastrous in South Africa and other arid regions that are water-scarce by nature due to low rainfall and water sources. According to some studies, droughts are not uncommon in Africa's drylands and have been rising in dry African terrain. Warm to hot summers and cool to cold winters describe the climate of the Free State Province, South Africa, a province that has been severely affected by drought events in recent times. Several studies have been carried out as regards drought prediction and mapping in arid and semi-arid areas using various models, tools and techniques. However, the use of machine learning algorithms is just emerging, especially in Sub-Saharan Africa. Studies have shown that machine learning and artificial intelligence methods have a high potential for assessment, prediction and identification of extreme events such as drought. Hence, this study aimed to evaluate drought dynamics in the Free State Province and identify drought drivers using regression-based algorithms. Results revealed that 2015 was severely affected by drought episodes as the study area observed extreme drought. More so, findings from this study showed that agricultural lands, cultivated grasslands, and barren surfaces were influenced or impacted by the drought disaster, especially in 2015, a drought year in the Free State Province. From the feature selection results, the influence of climate proxies and anthropogenic factors on VCI shows the ecological situation within the Free State Province.
英文关键词	Drought disaster Land use dynamics Drought drivers Machine learning Regression-based algorithms
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000750348100001
WOS关键词	CLIMATE-CHANGE ; AGRICULTURAL DROUGHT ; UNITED-STATES ; MACHINE ; IMPACT ; PREDICTION ; FORECAST ; IMPROVE ; FARMERS ; LOSSES
WOS类目	Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences ; Water Resources
WOS研究方向	Geology ; Meteorology & Atmospheric Sciences ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/377061
作者单位	[Orimoloye, Israel R.; Ololade, Olusola O.] Univ Free State, Ctr Environm Management, Bloemfontein, South Africa; [Orimoloye, Israel R.; Belle, Johanes A.] Univ Free State, Disaster Management Training & Educ Ctr Africa, Bloemfontein, South Africa; [Orimoloye, Israel R.] Univ Ft Hare, Dept Geog & Environm Sci, Private Bag X1314, ZA-5700 Alice, Eastern Cape Pr, South Africa; [Olusola, Adeyemi O.] Univ Free State, Dept Geog, Bloemfontein, South Africa; [Olusola, Adeyemi O.] Univ Ibadan, Dept Geog, Ibadan, Nigeria; [Pande, Chaitanya B.] St Gadge Baba Amravati Univ & Dr PDKV Akola, Amravati, India
推荐引用方式 GB/T 7714	Orimoloye, Israel R.,Olusola, Adeyemi O.,Belle, Johanes A.,et al. Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms[J],2022.
APA	Orimoloye, Israel R.,Olusola, Adeyemi O.,Belle, Johanes A.,Pande, Chaitanya B.,&Ololade, Olusola O..(2022).Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms.NATURAL HAZARDS.
MLA	Orimoloye, Israel R.,et al."Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms".NATURAL HAZARDS (2022).