干旱区生态环境知识资源中心(Arid): Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration

Arid

DOI	10.1007/s13201-022-01667-7
	Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration
	Elbeltagi, Ahmed; Raza, Ali; Hu, Yongguang; Al-Ansari, Nadhir; Kushwaha, N. L.; Srivastava, Aman; Vishwakarma, Dinesh Kumar; Zubair, Muhammad
通讯作者	Elbeltagi, A
来源期刊	APPLIED WATER SCIENCE
ISSN	2190-5487
EISSN	2190-5495
出版年	2022
卷号	12 期号:7
英文摘要	For developing countries, scarcity of climatic data is the biggest challenge, and model development with limited meteorological input is of critical importance. In this study, five data intelligent and hybrid metaheuristic machine learning algorithms, namely additive regression (AR), AR-bagging, AR-random subspace (AR-RSS), AR-M5P, and AR-REPTree, were applied to predict monthly mean daily reference evapotranspiration (ET0). For this purpose, climatic data of two meteorological stations located in the semi-arid region of Pakistan were used from the period 1987 to 2016. The climatic dataset includes maximum and minimum temperature (T-max, T-min), average relative humidity (RHavg), average wind speed (U-x), and sunshine hours (n). Sensitivity analysis through regression methods was applied to determine effective input climatic parameters for ET0 modeling. The results of performed regression analysis on all input parameters proved that T-min, RHAvg, U-x, and n were identified as the most influential input parameters at the studied station. From the results, it was revealed that all the selected models predicted ET0 at both stations with greater precision. The AR-REPTree model was located furthest and the AR-M5P model was located nearest to the observed point based on the performing indices at both the selected meteorological stations. The study concluded that under the aforementioned methodological framework, the AR-M5P model can yield higher accuracy in predicting ET0 values, as compared to other selected algorithms.
英文关键词	Reference evapotranspiration Additive regression Sensitivity and regression analysis Machine learning Hydrological modeling
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:000791796200001
WOS关键词	SUPPORT VECTOR MACHINE ; RANDOM SUBSPACE METHOD ; DIFFERENT CLIMATES ; NEURAL-NETWORKS ; MODELS ; SVM
WOS类目	Water Resources
WOS研究方向	Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391880
推荐引用方式 GB/T 7714	Elbeltagi, Ahmed,Raza, Ali,Hu, Yongguang,et al. Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration[J],2022,12(7).
APA	Elbeltagi, Ahmed.,Raza, Ali.,Hu, Yongguang.,Al-Ansari, Nadhir.,Kushwaha, N. L..,...&Zubair, Muhammad.(2022).Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration.APPLIED WATER SCIENCE,12(7).
MLA	Elbeltagi, Ahmed,et al."Data intelligence and hybrid metaheuristic algorithms-based estimation of reference evapotranspiration".APPLIED WATER SCIENCE 12.7(2022).