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DOI	10.3390/w13233379
	Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods
	Adnan, Rana Muhammad; Mostafa, Reham R.; Islam, Abu Reza Md. Towfiqul; Gorgij, Alireza Docheshmeh; Kuriqi, Alban; Kisi, Ozgur
通讯作者	Kisi, O (corresponding author), Univ Appl Sci, Dept Civil Engn, D-23562 Lubeck, Germany. ; Kisi, O (corresponding author), Ilia State Univ, Dept Civil Engn, Tbilisi 0162, Georgia.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:23
英文摘要	Drought modeling is essential in water resources planning and management in mitigating its effects, especially in arid regions. Climate change highly influences the frequency and intensity of droughts. In this study, new hybrid methods, the random vector functional link (RVFL) integrated with particle swarm optimization (PSO), the genetic algorithm (GA), the grey wolf optimization (GWO), the social spider optimization (SSO), the salp swarm algorithm (SSA) and the hunger games search algorithm (HGS) were used to forecast droughts based on the standard precipitation index (SPI). Monthly precipitation data from three stations in Bangladesh were used in the applications. The accuracy of the methods was compared by forecasting four SPI indices, SPI3, SPI6, SPI9, and SPI12, using the root mean square errors (RMSE), the mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and the determination coefficient (R-2). The HGS algorithm provided a better performance than the alternative algorithms, and it considerably improved the accuracy of the RVFL method in drought forecasting; the improvement in RMSE for the SPI3, SP6, SPI9, and SPI12 was by 6.14%, 11.89%, 14.14%, 24.5% in station 1, by 6.02%, 17.42%, 13.49%, 24.86% in station 2 and by 7.55%, 26.45%, 15.27%, 13.21% in station 3, respectively. The outcomes of the study recommend the use of a HGS-based RVFL in drought modeling.
英文关键词	drought modeling standard precipitation index random vector functional link hunger games search algorithm
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000734661800001
WOS关键词	PREDICTION ; OPTIMIZATION ; ALGORITHM ; SPI ; IMPLEMENTATION ; TEMPERATURE ; CHALLENGES ; REGRESSION
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373944
作者单位	[Adnan, Rana Muhammad] Guangzhou Univ, Sch Econ & Stat, Guangzhou 510006, Peoples R China; [Adnan, Rana Muhammad] Hohai Univ, State Key Lab Hydrol Water Resources & Hydraul En, Nanjing 210098, Peoples R China; [Mostafa, Reham R.] Mansoura Univ, Fac Comp & Informat Sci, Informat Syst Dept, Mansoura 35516, Egypt; [Islam, Abu Reza Md. Towfiqul] Begum Rokeya Univ, Dept Disaster Management, Rangpur 5400, Bangladesh; [Gorgij, Alireza Docheshmeh] Univ Sistan & Baluchestan, Fac Ind & Min Khash, Zahedan 9816745845, Iran; [Kuriqi, Alban] Univ Lisbon, Inst Super Tecn, CERIS, P-1049001 Lisbon, Portugal; [Kisi, Ozgur] Univ Appl Sci, Dept Civil Engn, D-23562 Lubeck, Germany; [Kisi, Ozgur] Ilia State Univ, Dept Civil Engn, Tbilisi 0162, Georgia
推荐引用方式 GB/T 7714	Adnan, Rana Muhammad,Mostafa, Reham R.,Islam, Abu Reza Md. Towfiqul,et al. Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods[J],2021,13(23).
APA	Adnan, Rana Muhammad,Mostafa, Reham R.,Islam, Abu Reza Md. Towfiqul,Gorgij, Alireza Docheshmeh,Kuriqi, Alban,&Kisi, Ozgur.(2021).Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods.WATER,13(23).
MLA	Adnan, Rana Muhammad,et al."Improving Drought Modeling Using Hybrid Random Vector Functional Link Methods".WATER 13.23(2021).