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

Min Wu,Qi Feng,Xiaohu Wen,Ravinesh C Deo,Linshan Yang,Zhenliang Yin,Danrui Sheng

doi:10.2166/nh.2020.012

Abstract

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.

Highlights

Evapotranspiration (ET) is the process of transfer of water from the surface of the earth to the atmosphere including evaporation and transpiration (Shiri et al ; Nourani et al ), and often used to estimate actual evapotranspiration in water balance studies and water resources management (Tao et al )
The performance of random forest (RF) model for Penman–Monteith FAO 56 (PMF-56) ET0 applied to the studied stations for the training and testing periods are summarized in Tables 3 and 4, which demonstrate the precision of the proposed RF model by the formulae of r, root mean squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency coefficient (NS)
This research selected the criteria during the testing phase to compare the capabilities of these models in the prediction of PMF-56 ET0 and all of the following analyses were performed in the testing period

Summary

Introduction

Evapotranspiration (ET) is the process of transfer of water from the surface of the earth to the atmosphere including evaporation and transpiration (Shiri et al ; Nourani et al ), and often used to estimate actual evapotranspiration in water balance studies and water resources management (Tao et al ). Knowledge of crop-water demands is an important practical consideration for improved wateruse efficiency (Benli et al ) This is because ET is a primary source of water loss, so its accurate evaluation can provide valuable information for water balance, irrigation system design, and water resources management (Torres et al ; Wen et al ). Some restrictions still exist in the application of PMF-56 equation, for example, it is difficult to obtain all meteorological data required in the estimation process, in a developing country, where the number of meteorological stations is limited and weather data records could be scarce (Abdullah et al ) Within this context, an alternative data-driven model which requires available input variables is necessary and significant

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