干旱区生态环境知识资源中心(Arid): Assessment and prediction of meteorological drought using machine learning algorithms and climate data

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DOI	10.1016/j.crm.2024.100630
	Assessment and prediction of meteorological drought using machine learning algorithms and climate data
	En-Nagre, Khalid; Aqnouy, Mourad; Ouarka, Ayoub; Naqvi, Syed Ali Asad; Bouizrou, Ismail; El Messari, Jamal Eddine Stitou; Tariq, Aqil; Soufan, Walid; Li, Wenzhao; El-Askary, Hesham
通讯作者	Tariq, A
来源期刊	CLIMATE RISK MANAGEMENT
ISSN	2212-0963
出版年	2024
卷号	45
英文摘要	Monitoring drought in semi-arid regions due to climate change is of paramount importance. This study, conducted in Morocco's Upper Draa Basin (UDB), analyzed data spanning from 1980 to 2019, focusing on the calculation of drought indices, specifically the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) at multiple timescales (1, 3, 9, 12 months). Trends were assessed using statistical methods such as the MannKendall test and the Sen's Slope estimator. Four significant machine learning (ML) algorithms, including Random Forest, Voting Regressor, AdaBoost Regressor, and K-Nearest Neighbors Regressor, were evaluated to predict the SPEI values for both three and 12-month periods. The algorithms' performance was measured using statistical indices. The study revealed that drought distribution within the UDB is not uniform, with a discernible decreasing trend in SPEI values. Notably, the four ML algorithms effectively predicted SPEI values for the specified periods. Random Forest, Voting Regressor, and AdaBoost demonstrated the highest Nash-Sutcliffe Efficiency (NSE) values, ranging from 0.74 to 0.93. In contrast, the K-Nearest Neighbors algorithm produced values within the range of 0.44 to 0.84. These research findings have the potential to provide valuable insights for water resource management experts and policymakers. However, it is imperative to enhance data collection methodologies and expand the distribution of measurement sites to improve data representativeness and reduce errors associated with local variations.
英文关键词	Drought Machine learning Upper Draa Random Forest SPEI
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:001283934000001
WOS关键词	MANN-KENDALL ; HIGH ATLAS ; PRECIPITATION ; REGRESSION ; BOOTSTRAP ; COVER ; TESTS ; SCALE ; SPEI
WOS类目	Environmental Sciences ; Environmental Studies ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403210
推荐引用方式 GB/T 7714	En-Nagre, Khalid,Aqnouy, Mourad,Ouarka, Ayoub,et al. Assessment and prediction of meteorological drought using machine learning algorithms and climate data[J],2024,45.
APA	En-Nagre, Khalid.,Aqnouy, Mourad.,Ouarka, Ayoub.,Naqvi, Syed Ali Asad.,Bouizrou, Ismail.,...&El-Askary, Hesham.(2024).Assessment and prediction of meteorological drought using machine learning algorithms and climate data.CLIMATE RISK MANAGEMENT,45.
MLA	En-Nagre, Khalid,et al."Assessment and prediction of meteorological drought using machine learning algorithms and climate data".CLIMATE RISK MANAGEMENT 45(2024).