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 (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.
Highlights
Evaluation of reference evapotranspiration (ETo) plays an important and undeniable role in irrigation scheduling, drought analysis, climate change studies, water level balance, agricultural and forest meteorology, long-term decision-making in food and water security policies, and optimum allocation of water resources [1,2,3]
GA5 is obtained, which is approximately equal to GA8 (RMSE = 0.3263 mm day−1 ) with less input data
GA5 estimated ETo throughout Iran with more accuracy than GA2, GA3, and GA4. This means that the wind speed (WS) was introduced as the most important factor to control the dynamics of ETo process throughout Iran
Summary
Evaluation of reference evapotranspiration (ETo) plays an important and undeniable role in irrigation scheduling, drought analysis, climate change studies, water level balance, agricultural and forest meteorology, long-term decision-making in food and water security policies, and optimum allocation of water resources [1,2,3]. Several methods have been developed to predict ETo throughout the world, there is a limited number of models to estimate ETo where meteorological data is restricted or insufficient [1,2]. A solution to deal with this limitation is to use data-driven machine learning techniques, genetic approaches, including genetic algorithm (GA) and gene expression programming (GEP). The GA and GEP methods have been developed in various aspects of water resources such as streamflow forecasting [4], rainfall–runoff modeling [5,6,7,8], modeling transport streams with suspended. 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
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