Estimating the Pan Evaporation in Northwest China by Coupling CatBoost with Bat Algorithm
نویسندگان
چکیده
Accurate estimation of pan evaporation (Ep) is vital for the development water resources and agricultural management, especially in arid semi-arid regions where it restricted to set up facilities measure accurately consistently. Besides, using estimating models coefficient (kp) a classic method assess reference evapotranspiration (ET0) which indispensable crop growth, irrigation scheduling, economic assessment. This study estimated potential novel hybrid machine learning model Coupling Bat algorithm (Bat) Gradient boosting with categorical features support (CatBoost) daily northwest China. Two other commonly used algorithms including random forest (RF) original CatBoost (CB) were also applied comparison. The meteorological data 12 years (2006–2017) from 45 weather stations areas China, minimum maximum air temperature (Tmin, Tmax), relative humidity (RH), wind speed (U), global solar radiation (Rs), utilized feed three exploring ability predicting evaporation. results revealed that new developed Bat-CB (RMSE = 0.859–2.227 mm·d?1; MAE 0.540–1.328 NSE 0.625–0.894; MAPE 0.162–0.328) was superior RF CB. In addition, CB 0.897–2.754 0.531–1.77 0.147–0.869; 0.161–0.421) slightly outperformed 1.005–3.604 0.644–2.479 ?1.242–0.894; 0.176–0.686) had poor operate erratic changes Furthermore, improvement presented more comprehensively obviously seasonal spatial performance compared RF. Overall, has high accuracy, robust stability, huge Ep China applications findings this have equal significance adjacent countries.
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ژورنال
عنوان ژورنال: Water
سال: 2021
ISSN: ['2073-4441']
DOI: https://doi.org/10.3390/w13030256