干旱区生态环境知识资源中心(Arid): CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China

Arid

DOI	10.1016/j.jhydrol.2020.125087
	CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China
	Zhang, Yixiao; Zhao, Zhongguo; Zheng, Jianghua
通讯作者	Zheng, JH
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2020
卷号	588
英文摘要	Establishing a computational model for accurate prediction of reference crop evapotranspiration (ET0) is critical for regional water resources planning and irrigation scheduling design. FAO Penman-Monteith equation is recommended as the standard model to predict ET0. However, its application is restricted by lack of complete meteorological data in many regions. This study evaluated the performance of CatBoost, an algorithm for gradient boosting on decision trees, for estimating daily ET0 using limited meteorological data in arid and semi-arid regions of Northern China. The CatBoost model was further compared with their corresponding generalized regression neural network (GRNN) and random forests (RF) models. Eight input combinations of daily meteorological data including daily maximum air temperature (T-max), daily minimum air temperature (T-min), wind speed at 2 m height (u(2)), relative humidity (RH) and net radiation (R-n) from 15 weather stations during 1996-2015 were used to train and WA the models. Four statistical indicators were used to evaluate the accuracy and performance of the models, including coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE). The results showed that all the three models using T-max, T-min, u(2) and R-n could obtain satisfactory ET0 estimates in arid and semi-arid regions of Northern China with incomplete sets of data. For the local models, CatBoost (on average RMSE ranging 0.096-0.821 mm d(-1)) was superior to GRNN (on average RMSE ranging 0.206-0.847 mm d(-1)) and RF (on average RMSE ranging 0.169-0.866 mm d(-1)) under the same meteorological parameters as input. The results of the generalized models were similar to the local models, but the former ones performed worse than the latter ones. Overall, CatBoost is observed to be the best alternative for estimating ET0, which is helpful for irrigation scheduling in arid and semiarid regions of Northern China and maybe elsewhere with similar climates.
英文关键词	Reference crop evapotranspiration CatBoost Random forests Generalized regression neural network Arid and semi-arid regions of Northern China
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000568826300018
WOS关键词	GLOBAL SOLAR-RADIATION ; MODELING REFERENCE EVAPOTRANSPIRATION ; LIMITED CLIMATIC DATA ; REGRESSION NEURAL-NETWORKS ; EVAPORATION ; PERFORMANCE ; PREDICTION ; EQUATIONS ; ANFIS ; SVM
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326243
作者单位	[Zhang, Yixiao; Zhao, Zhongguo; Zheng, Jianghua] Key Lab Oasis Ecol Educ, Urumqi, Xinjiang, Peoples R China; [Zhang, Yixiao; Zhao, Zhongguo; Zheng, Jianghua] Xinjiang Univ, Coll Resources & Environm Sci, Urumqi, Xinjiang, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yixiao,Zhao, Zhongguo,Zheng, Jianghua. CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China[J]. 新疆大学,2020,588.
APA	Zhang, Yixiao,Zhao, Zhongguo,&Zheng, Jianghua.(2020).CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China.JOURNAL OF HYDROLOGY,588.
MLA	Zhang, Yixiao,et al."CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China".JOURNAL OF HYDROLOGY 588(2020).