干旱区生态环境知识资源中心(Arid): Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data

Arid

DOI	10.1080/10106049.2022.2158951
	Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data
	Adnan, Rana Muhammad; Dai, Hong-Liang; Mostafa, Reham R.; Islam, Abu Reza Md. Towfiqul; Kisi, Ozgur; Heddam, Salim; Zounemat-Kermani, Mohammad
通讯作者	Dai, HL
来源期刊	GEOCARTO INTERNATIONAL
ISSN	1010-6049
EISSN	1752-0762
出版年	2023
卷号	38 期号:1
英文摘要	The accurate assessment of groundwater levels is critical to water resource management. With global warming and climate change, its significance has become increasingly evident, particularly in arid and semi-arid areas. This study compares new extreme learning machines (ELM) methods tuned with metaheuristic algorithms such as particle swarm optimization, grey wolf optimization, the whale optimization algorithm (WOA), Harris Hawks optimizer (HHO), and the jellyfish search optimizer (JFO) in groundwater level estimation. Daily precipitation and temperature datasets acquired from two stations in northern Bangladesh were used as inputs to the models, which were evaluated based on different quantitative statistics and assessed based on RMSE, MAE, R-2, and some new graphical inspection methods. The outcomes of the applications revealed that the efficiency of ELM models was considerably improved by using metaheuristic algorithms. The ELM-JSO improved the RMSE of the standalone ELM model by 13% for the optimal precipitation, temperature, and groundwater level inputs in the testing stage. Among the implemented methods, the ELM-JFO performed the best in estimating the daily groundwater level, and the ELM-WOA and ELM-HHO, respectively, followed it. Viability of a new extreme machine learning (ELM) method tuned with Jellyfish search optimizer (JFO) is investigated in groundwater level estimation. The ELM-JFO is compared with hybrid ELM-PSO, ELM-WOA and ELM-HHO models using daily precipitation and temperature data acquired from two stations of Bangladesh. The ELM-JSO improves the root mean square error of the standalone ELM model by 13% for the optimal precipitation, temperature and groundwater level inputs.
英文关键词	Groundwater prediction extreme learning machine particle swarm optimization grey wolf optimization whale optimization algorithm Harris Hawks optimizer jellyfish search optimizer
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000904660800001
WOS关键词	EXTREME LEARNING-MACHINE ; ARTIFICIAL NEURAL-NETWORKS ; CHANGE IMPACT ASSESSMENT ; SUPPORT VECTOR MACHINES ; FUZZY INFERENCE SYSTEM ; RISK-ASSESSMENT ; PART II ; PREDICTION ; ENSEMBLE ; SIMULATION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396672
推荐引用方式 GB/T 7714	Adnan, Rana Muhammad,Dai, Hong-Liang,Mostafa, Reham R.,et al. Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data[J],2023,38(1).
APA	Adnan, Rana Muhammad.,Dai, Hong-Liang.,Mostafa, Reham R..,Islam, Abu Reza Md. Towfiqul.,Kisi, Ozgur.,...&Zounemat-Kermani, Mohammad.(2023).Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data.GEOCARTO INTERNATIONAL,38(1).
MLA	Adnan, Rana Muhammad,et al."Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data".GEOCARTO INTERNATIONAL 38.1(2023).