Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation

Reference evapotranspiration (ET0) is one of the most important parameters, which is required in many fields such as hydrological, agricultural, and climatological studies. Therefore, its estimation via reliable and accurate techniques is a necessity. The present study aims to estimate the monthly ET0 time series of six stations located in Iran. To achieve this objective, gene expression programming (GEP) and support vector regression (SVR) were used as standalone models. A novel hybrid model was then introduced through coupling the classical SVR with an optimization algorithm, namely intelligent water drops (IWD) (i.e., SVR-IWD). Two various types of scenarios were considered, including the climatic dataand antecedent ET0 data-based patterns. In the climatic data based models, the effective climatic parameters were recognized by using two pre-processing techniques consisting of tau Kendall and entropy. It is worthy to mention that developing the hybrid SVR-IWD model as well as utilizing the tau Kendall and entropy approaches to discern the most influential weather parameters on ET0 are the innovations of current research. The results illustrated that the applied pre-processing methods introduced different climatic inputs to feed the models. The overall results of present study revealed that the proposed hybrid SVR-IWD model outperformed the standalone SVR one under both the considered scenarios when estimating the monthly ET0. In addition to the mentioned models, two types of empirical equations were also used including the Hargreaves-Samani (H-S) and Priestley-Taylor (P-T) in their original and calibrated versions. It was concluded that the calibrated versions showed superior performances compared to their original ones.

Automated summary

This study aimed to estimate the monthly ET0 time series for six stations in Iran using gene expression programming (GEP) and support vector regression (SVR) as standalone models. A new hybrid model, SVR-IWD, was introduced by coupling SVR with an optimization algorithm. Pre-processing techniques of tau Kendall and entropy were used to identify influential weather parameters on ET0. The results showed that the hybrid SVR-IWD model outperformed the standalone SVR model in estimating monthly ET0 under both scenarios.