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Article

Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm

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State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
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Nanchang Institute of Technology, Nanchang 330099, China
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State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
4
Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany
*
Authors to whom correspondence should be addressed.
Water 2021, 13(3), 256; https://doi.org/10.3390/w13030256
Received: 9 December 2020 / Revised: 18 January 2021 / Accepted: 18 January 2021 / Published: 21 January 2021
(This article belongs to the Section Hydrology)
Accurate estimation of pan evaporation (Ep) 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 (Tmin, Tmax), relative humidity (RH), wind speed (U), and global solar radiation (Rs), 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·d−1; MAE = 0.540–1.328 mm·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·d−1; MAE = 0.531–1.77 mm·d−1; NSE = 0.147–0.869; MAPE = 0.161–0.421) slightly outperformed RF (RMSE = 1.005–3.604 mm·d−1; MAE = 0.644–2.479 mm·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 Ep estimation in arid and semi-arid regions of northwest China and the applications of findings in this study have equal significance for adjacent countries. View Full-Text
Keywords: pan evaporation; machine learning; bat algorithm; CatBoost; random forest pan evaporation; machine learning; bat algorithm; CatBoost; random forest
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MDPI and ACS Style

Dong, L.; Zeng, W.; Wu, L.; Lei, G.; Chen, H.; Srivastava, A.K.; Gaiser, T. Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm. Water 2021, 13, 256. https://doi.org/10.3390/w13030256

AMA Style

Dong L, Zeng W, Wu L, Lei G, Chen H, Srivastava AK, Gaiser T. Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm. Water. 2021; 13(3):256. https://doi.org/10.3390/w13030256

Chicago/Turabian Style

Dong, Liming, Wenzhi Zeng, Lifeng Wu, Guoqing Lei, Haorui Chen, Amit K. Srivastava, and Thomas Gaiser. 2021. "Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm" Water 13, no. 3: 256. https://doi.org/10.3390/w13030256

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