干旱区生态环境知识资源中心(Arid): Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

Arid

DOI	10.3390/w13030256
	Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm
	Dong, Liming; Zeng, Wenzhi; Wu, Lifeng; Lei, Guoqing; Chen, Haorui; Srivastava, Amit Kumar; Gaiser, Thomas
通讯作者	Zeng, WZ (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. ; Wu, LF (corresponding author), Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China.
来源期刊	WATER
EISSN	2073-4441
出版年	2021
卷号	13 期号:3
英文摘要	Accurate estimation of pan evaporation (E-p) is vital for the development of water resources and agricultural water management, especially in arid and semi-arid regions where it is restricted to set up the facilities and measure pan evaporation accurately and consistently. Besides, using pan evaporation estimating models and pan coefficient (kp) models is a classic method to assess the reference evapotranspiration (ET0) which is indispensable to crop growth, irrigation scheduling, and economic assessment. This study estimated the potential of a novel hybrid machine learning model Coupling Bat algorithm (Bat) and Gradient boosting with categorical features support (CatBoost) for estimating daily pan evaporation in arid and semi-arid regions of northwest China. Two other commonly used algorithms including random forest (RF) and original CatBoost (CB) were also applied for comparison. The daily meteorological data for 12 years (2006-2017) from 45 weather stations in arid and semi-arid areas of China, including minimum and maximum air temperature (T-min, T-max), relative humidity (RH), wind speed (U), and global solar radiation (R-s), were utilized to feed the three models for exploring the ability in predicting pan evaporation. The results revealed that the new developed Bat-CB model (RMSE = 0.859-2.227 mm center dot d(-1); MAE = 0.540-1.328 mm center dot d(-1); NSE = 0.625-0.894; MAPE = 0.162-0.328) was superior to RF and CB. In addition, CB (RMSE = 0.897-2.754 mm center dot d(-1); MAE = 0.531-1.77 mm center dot d(-1); NSE = 0.147-0.869; MAPE = 0.161-0.421) slightly outperformed RF (RMSE = 1.005-3.604 mm center dot d(-1); MAE = 0.644-2.479 mm center dot d(-1); NSE = -1.242-0.894; MAPE = 0.176-0.686) which had poor ability to operate the erratic changes of pan evaporation. Furthermore, the improvement of Bat-CB was presented more comprehensively and obviously in the seasonal and spatial performance compared to CB and RF. Overall, Bat-CB has high accuracy, robust stability, and huge potential for E-p estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries.
英文关键词	pan evaporation machine learning bat algorithm CatBoost random forest
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:000615611600001
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/348266
作者单位	[Dong, Liming; Zeng, Wenzhi; Lei, Guoqing] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Wu, Lifeng] Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China; [Chen, Haorui] China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China; [Srivastava, Amit Kumar; Gaiser, Thomas] Univ Bonn, Inst Crop Sci & Resource Conservat INRES, Crop Sci Grp, Katzenburgweg 5, D-53115 Bonn, Germany
推荐引用方式 GB/T 7714	Dong, Liming,Zeng, Wenzhi,Wu, Lifeng,et al. Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm[J],2021,13(3).
APA	Dong, Liming.,Zeng, Wenzhi.,Wu, Lifeng.,Lei, Guoqing.,Chen, Haorui.,...&Gaiser, Thomas.(2021).Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm.WATER,13(3).
MLA	Dong, Liming,et al."Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm".WATER 13.3(2021).