干旱区生态环境知识资源中心(Arid): Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran

Arid

DOI	10.1016/j.compag.2020.105418
	Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran
	Seifi, Akram; Soroush, Fatemeh
通讯作者	Seifi, A
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2020
卷号	173
英文摘要	Pan evaporation (E-p) estimation is important in scheduling and computing irrigation water requirement. This study evaluated the ability of novel meta-heuristic optimization algorithms including Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Whale Optimization Algorithm (WOA) that hybridized with artificial neural networks (ANNs) in estimating daily E-p and optimizing explicit predictive equations. Five meteorological stations in five climate areas (Humid, sub-humid, semi-arid, arid, hyper-arid) located in Iran, and each station having ten scenarios of available input data, were used. The Subset Selection of Maximum Dissimilarity (SSMD) was used for pre-processing of data set and automatic selection of the training and testing subsets. The modeling results were compared based on the global performance indicator (GPI), root mean square error (RMSE), coefficient of determination (R-2), and Nash-Sutcliffe Efficiency (NSE) criteria in addition to Taylor diagram and Box-plots. The results indicated that the performance of models of ANN-GA-4 (14: T-mean, RH; R-2 = 0.83, RMSE = 0.95, NSE = 0.82, GPI= 0.68) for Astara station, ANN-GA-1 (I1: T-max; R-2 =0.79, RMSE =1.39, NSE = 0.78, GPI = 0.68) for Gorgan station, ANN-GA-5 (15: T-mean, n; R-2 = 0.86, RMSE =1.98, NSE = 0.86, GPI = 0.96) for Tabas station, ANN-6 (16: T-mean, U-2) for Esfahan (R-2 = 0.77, RMSE =1.57, NSE = 0.77, GPI = 0.6) and Hamedan (R-2 = 0.69, RMSE =1.43, NSE = 0.68, GPI =1.06) stations were superior than the other models with limited climatic data. After selecting best model in each station, the explicit optimized equations to calculate the daily E-p in each climate are provided as another major contribution of the present study.
英文关键词	Explicit equations Hybrid artificial neural network Meta-heuristic algorithms Optimization Pan evaporation loss
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000531080400044
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; SUPPORT VECTOR REGRESSION ; GENETIC ALGORITHMS ; PREDICTION ; PERFORMANCE ; MODELS ; CLASSIFICATION ; VARIABLES ; SELECTION ; MACHINE
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324419
作者单位	[Seifi, Akram; Soroush, Fatemeh] Vali E Asr Univ Rafsanjan, Coll Agr, Dept Water Sci & Engn, Rafsanjan, Iran
推荐引用方式 GB/T 7714	Seifi, Akram,Soroush, Fatemeh. Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran[J],2020,173.
APA	Seifi, Akram,&Soroush, Fatemeh.(2020).Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran.COMPUTERS AND ELECTRONICS IN AGRICULTURE,173.
MLA	Seifi, Akram,et al."Pan evaporation estimation and derivation of explicit optimized equations by novel hybrid meta-heuristic ANN based methods in different climates of Iran".COMPUTERS AND ELECTRONICS IN AGRICULTURE 173(2020).