干旱区生态环境知识资源中心(Arid): A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China

Arid

DOI	10.1016/j.jhydrol.2020.124545
	A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China
	Wang, Haomin 1,2; Yan, Hui 3; Zeng, Wenzhi 1; Lei, Guoqing 1; Ao, Chang 1; Zha, Yuanyuan 1
通讯作者	Zeng, Wenzhi
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2020
卷号	582
英文摘要	Accurate prediction of water surface evaporation (Ep) is important in the fields of both hydrology and irrigation engineering. This study evaluated the potential ability of a new hybrid model based on the salp swarm algorithm (SSA) and the kernel-based nonlinear Arps decline (KNEA) in predicting Ep. Two other common machine learning models, including the M5 model tree (M5) and the multivariate adaptive regression splines (MARS), were also applied in this study for comparison. All models were developed using daily records between 2000 and 2015 from 12 meteorological stations in the arid and semi-arid regions of northwest China. These daily records, including the maximum and minimum temperatures, solar radiation, wind speed and relative humidity, were randomly divided into two parts, with 70% of which used for model calibration and the others applied for validation. Four different parameter input combinations were equipped to explore the possibility of improving model accuracy. Two data application scenarios and five statistical indicators including the root-mean-square-error (RMSE), mean absolute error (MAE), scatter index (SI), d-index and determination coefficient (R-2) were used for model evaluation. In the scenario of using local data as inputs for model calibration and validation, the impacts of wind speed and relative humidity on Ep were both greater than that of solar radiation, and SSA-KNEA was consistently superior to MARS or M5 across all the input combinations. In the scenario of using cross-station data, in which models using the best input combination were developed by local data of each station but validated by data from each of the remaining 11 stations, SSA-KNEA models performed better than MARS or M5 models on average. In addition, the SSA-KNEA model established by data from Station 51777 was the most suitable generalized model in this research area. Overall, our findings suggested that the new hybrid algorithm (i.e., SSA-KNEA) has high potential for Ep estimation in the arid and semi-arid regions of China, with local or cross-station data.
英文关键词	Machine learning Cross-station Meta-heuristic algorithm Model comparison
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000517663700072
WOS关键词	SUPPORT VECTOR MACHINE ; GLOBAL SOLAR-RADIATION ; REFERENCE EVAPOTRANSPIRATION ; LS-SVR ; REGRESSION ; PARAMETERS ; CLIMATES ; LAKE ; AREA ; ANN
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314994
作者单位	1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; 2.Heiloneiang Inst Water Conservancy & Hydropower S, Haerbin 100120, Peoples R China; 3.Henan Univ Sci & Technol, Coll Agr Engn, Luoyang 471003, Peoples R China
推荐引用方式 GB/T 7714	Wang, Haomin,Yan, Hui,Zeng, Wenzhi,et al. A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China[J],2020,582.
APA	Wang, Haomin,Yan, Hui,Zeng, Wenzhi,Lei, Guoqing,Ao, Chang,&Zha, Yuanyuan.(2020).A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China.JOURNAL OF HYDROLOGY,582.
MLA	Wang, Haomin,et al."A novel nonlinear Arps decline model with salp swarm algorithm for predicting pan evaporation in the arid and semi-arid regions of China".JOURNAL OF HYDROLOGY 582(2020).