Multivariate modeling of pan evaporation in monthly temporal resolution using a hybrid evolutionary data-driven method (case study: Urmia Lake and Gavkhouni basins).

Evaporation is an important meteorological variable that has a great impact on water resources. In the current research, climatology data, and seasonal coefficient have been used to estimate monthly pan evaporation (E) for 2005-2018 study years at four selected stations of the Urmia Lake basin with Dsa and six selected stations of Gavkhouni basin with Bsk climate categories, in Iran. Estimation of monthly E was performed using data-driven methods such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) as well as wavelet-hybrids (WANN, WANFIS, and WGEP).