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Open AccessArticle

A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

1
Department of Agricultural and Biological Engineering, Indian River Research and Education Center, University of Florida, Fort Pierce, FL 34945, USA
2
Department of Water Engineering, Sari Agricultural Sciences and Natural Resources University, Sari, Iran
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(6), 311; https://doi.org/10.3390/atmos10060311
Received: 2 May 2019 / Revised: 24 May 2019 / Accepted: 1 June 2019 / Published: 5 June 2019
(This article belongs to the Special Issue Evapotranspiration Observation and Prediction: Uncertainty Analysis)
In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day−1) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day−1, and Kerman (KE), RMSE = 0.4177 mm day−1, respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R2 < 0.50) due to its poor performance under such extreme conditions. View Full-Text
Keywords: machine learning; crop water requirement; Iran; hydrological extremes; uncertainty; weather parameters machine learning; crop water requirement; Iran; hydrological extremes; uncertainty; weather parameters
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Valipour, M.; Gholami Sefidkouhi, M.A.; Raeini-Sarjaz, M.; Guzman, S.M. A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates. Atmosphere 2019, 10, 311.

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