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

Abstract

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 (R 2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R 2 < 0.50) due to its poor performance under such extreme conditions. Atmosphere 2019, 10, 311 2 of 15 sediment [9], predicting velocity in compound channels [10], characterizing risks in water supply systems [11], and modeling evaporation [12].Although the GEP has been developed in water resources studies, the application of this technique for ETo modeling is limited. Some of the successful applications of the GEP to estimate ETo can be listed as follows.Parasuraman et al. [3] modeled ETo using only ground temperature and net radiation. Irmak and Kamble [13] investigated evapotranspiration data assimilation with the GA and soil, water, atmosphere, and plant (SWAP) model for on-demand irrigation. The data assimilation methodology obtained from the present research can be considered a practical tool at the field scale for scheduling the irrigation estimated by remote sensing-based evapotranspiration. The results showed that the GA was effectively able to determine the terms included in the fitness function, and parameters were predicted reasonably, especially if only four variables were included. Traore and Guven [14] developed regional-specific numerical models of ETo using the GEP in Sahel. Statistically, the GEP was an effectual modeling tool for the successful computation of ETo under the study area. The results indicated that, using the GEP model, it would be possible to formulate an accurate and applicable numerical equation for each region by irrigation systems for the manual computation of ETo under the study area, where sufficient meteorological variable is often missing. Shiri [15] claimed that GEP outperforms empirical model to calculate ETo in hyper arid regions over Iran. Traore et al. [16] found that GEP is a robust tool to model ETo in in Jiangsu province, China. Mattar [17] demonstrated that GEP is a more accurate method than empirical equations to ...

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