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

Wu, Min and Feng, Qi and Wen, Xiaohu and Deo, Ravinesh C. ORCID: https://orcid.org/0000-0002-2290-6749 and Yin, Zhenliang and Yang, Linshan and Sheng, Danrui (2020) Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region. Hydrology Research: an International Journal. ISSN 1998-9563

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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 appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
Faculty/School / Institute/Centre: Current - Institute for Advanced Engineering and Space Sciences (1 Aug 2018 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sept 2019 -)
Date Deposited: 16 Jul 2020 04:00
Last Modified: 17 Jul 2020 05:02
Uncontrolled Keywords: arid areas, evapotranspiration, Monte Carlo, predict, random forest
Fields of Research (2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems
05 Environmental Sciences > 0502 Environmental Science and Management > 050299 Environmental Science and Management not elsewhere classified
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.2166/nh.2020.012
URI: http://eprints.usq.edu.au/id/eprint/38947

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