Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

• Published in: Hydrology Research Vol. 51; no. 4; pp. 648 - 665
• Main Authors: Wu, Min, Feng, Qi, Wen, Xiaohu, Deo, Ravinesh C., Yin, Zhenliang, Yang, Linshan, Sheng, Danrui
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.08.2020
• Subjects: Accuracy; Air temperature; Algorithms; arid areas; Arid regions; Artificial intelligence; Computer simulation; Evapotranspiration; Evapotranspiration estimates; Forest management; Irrigation; Machine learning; Meteorological data; Methods; monte carlo; Monte Carlo simulation; Neural networks; Oases; predict; Prediction models; random forest; Regression analysis; Relative humidity; Statistical methods; Support vector machines; Uncertainty; Uncertainty analysis; Variables; Wind speed; China

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an app...