干旱区生态环境知识资源中心(Arid): Developing the artificial neural network-evolutionary algorithms hybrid models (ANN-EA) to predict the daily evaporation from dam reservoirs

Arid

DOI	10.1007/s00366-021-01523-3
	Developing the artificial neural network-evolutionary algorithms hybrid models (ANN-EA) to predict the daily evaporation from dam reservoirs
	Azar, Naser Arya; Kardan, Nazila; Milan, Sami Ghordoyee
通讯作者	Kardan, N (corresponding author), Azarabaijan Shahid Madani Univ, Dept Civil Engn, Tabriz, Iran.
来源期刊	ENGINEERING WITH COMPUTERS
ISSN	0177-0667
EISSN	1435-5663
出版年	2021-11
英文摘要	Evaporation is one of the key factors in the hydrological cycle, and it is one of the most critical parameters in hydrological, agricultural, and meteorological studies, especially in arid and semi-arid regions. By estimating evaporation, it is possible to make a significant contribution to studies related to water balance, management and design of irrigation systems, estimation of crop amount, and management of water resources. In this regard, in the present study, using artificial neural network (ANN) and its hybrid algorithms including Harris Hawks Optimization (HHO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO), the daily evaporation value from the reservoir of Qaleh Chay Ajab Shir dam was estimated. For this purpose, the effective parameters in the evaporation process were introduced to each of the models in the form of different input patterns, and the evaporation value from the reservoir was also considered as output parameter. The results showed that the best selected model for ANN is the P3 model including three parameters of minimum air temperature, and daily evaporation data with NASH of 0.89, RMSE of 1.5 mm/day, and MAE of 1.1 mm/day, which was optimized by applying hybrid algorithms to train the neural network. The results disclosed that all three models had a good performance in estimating the daily evaporation value, so that the value of the correlation coefficient for all three models is in the range of 0.95-0.99. Based on evaluation criteria, ANN-HHO has better performance than the two other algorithms in estimating daily evaporation value. The values of NASH, RMSE and MAE for the selected pattern of the test data are 0.943, 0.908 and 0.736 mm/day, respectively. For better analysis, Taylor diagram is used (RMSD = 0.98, CC = 0.97, STD = 4 for ANN-HHO). The results of this diagram also showed that the ANN-HHO model provides acceptable performance when compared with other models. Considering the promising results of the models in predicting the daily evaporation from dam, it is suggested to use the existing approach for landscaping the groundwater balance and design of irrigation systems.
英文关键词	Evaporation Dam reservoirs ANN-HHO ANN-PSO ANN-WOA Time series forecast
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000715016800001
WOS关键词	DAILY PAN EVAPORATION ; MULTILAYER PERCEPTRON ; FUZZY-LOGIC ; OPTIMIZATION ; LAKE ; EVAPOTRANSPIRATION ; LOSSES
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Mechanical
WOS研究方向	Computer Science ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/367705
作者单位	[Azar, Naser Arya] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz, Iran; [Kardan, Nazila] Azarabaijan Shahid Madani Univ, Dept Civil Engn, Tabriz, Iran; [Milan, Sami Ghordoyee] Univ Tehran, Dept Irrigat & Drainage Engn, Aburaihan Campus, Tehran, Iran
推荐引用方式 GB/T 7714	Azar, Naser Arya,Kardan, Nazila,Milan, Sami Ghordoyee. Developing the artificial neural network-evolutionary algorithms hybrid models (ANN-EA) to predict the daily evaporation from dam reservoirs[J],2021.
APA	Azar, Naser Arya,Kardan, Nazila,&Milan, Sami Ghordoyee.(2021).Developing the artificial neural network-evolutionary algorithms hybrid models (ANN-EA) to predict the daily evaporation from dam reservoirs.ENGINEERING WITH COMPUTERS.
MLA	Azar, Naser Arya,et al."Developing the artificial neural network-evolutionary algorithms hybrid models (ANN-EA) to predict the daily evaporation from dam reservoirs".ENGINEERING WITH COMPUTERS (2021).