干旱区生态环境知识资源中心(Arid): RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region

Arid

DOI	10.1016/j.suscom.2021.100514
	RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region
	Kamel, Ammar Hatem; Afan, Haitham Abdulmohsin; Sherif, Mohsen; Ahmed, Ali Najah; El-Shafie, Ahmed
通讯作者	Afan, HA (corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam.
来源期刊	SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS
ISSN	2210-5379
EISSN	2210-5387
出版年	2021
卷号	30
英文摘要	Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir' water system. In fact, the evaporation rate and more specifically from subsurface is considered as highly stochastic and non-linear process that affected by several natural variables. In this research, a focuses on the development of an Artificial Intelligence (AI) model, to predict the evaporation rate has been proposed. The model's input variables for this model include temperature, wind speed, humidity and water depth. In addition, two AI models have been employed to predict the sub-surface evaporation rate namely: Generalized Regression Neural Network (GRNN) and Radial Basis Function Neural Network (RBFNN) as a first attempt to utilize AI models in this topic. In order to substantiate the effectiveness of the AI model, the models have been applied utilizing actual hydrological and climatological in an arid region, for two soil types: fine gravel (F.G) and coarse gravel (C.G). The prediction accuracy of these models has been assessed through examining several statistical indicators. The results showed that the Artificial Neural Networks (ANN) model has the capacity for a highly accurate evaporation rate prediction, for the subsurface reservoir. The correlation coefficient for the fine gravel soil, and coarse gravel soil, was recorded as 0.936 and 0.959 respectively.
英文关键词	Prediction model Sub-surface reservoir Evaporation rate estimation Arid region Neural network model
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000663407600009
WOS关键词	ADAPTIVE NEURO-FUZZY ; SUPPORT VECTOR REGRESSION ; WIND-SPEED ; SENSITIVITY-ANALYSIS ; NETWORKS ; SIMULATION ; ALGORITHM ; ANN
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/351873
作者单位	[Kamel, Ammar Hatem] Univ Anbar, Dams & Water Resources Engn Coll Engn, Anbar, Iraq; [Afan, Haitham Abdulmohsin] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Sherif, Mohsen] United Arab Emirates Univ, Natl Water & Energy Ctr, POB 15551, Al Ain, U Arab Emirates; [Sherif, Mohsen] United Arab Emirates Univ, Coll Engn, Civil & Environm Engn Dept, POB 15551, Al Ain, U Arab Emirates; [Ahmed, Ali Najah] Univ Tenaga Nas, Inst Energy Infrastruct IEI, Kajang 43000, Selangor, Malaysia; [El-Shafie, Ahmed] Univ Malaya, Fac Engn, Dept Civil Engn, Kuala Lumpur, Malaysia
推荐引用方式 GB/T 7714	Kamel, Ammar Hatem,Afan, Haitham Abdulmohsin,Sherif, Mohsen,et al. RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region[J],2021,30.
APA	Kamel, Ammar Hatem,Afan, Haitham Abdulmohsin,Sherif, Mohsen,Ahmed, Ali Najah,&El-Shafie, Ahmed.(2021).RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region.SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS,30.
MLA	Kamel, Ammar Hatem,et al."RBFNN versus GRNN modeling approach for sub-surface evaporation rate prediction in arid region".SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 30(2021).