Comprehensive assessment of 12 soft computing approaches for modelling reference evapotranspiration in humid locations

Abstract Owing to the lack of lysimetric data in many regions, the standard Penman–Monteith equation‐adopted by Food and Agriculture Organization of the United Nations (FAO56‐PM model) is usually used to calculate the reference evapotranspiration (ET o ). However, as this model needs many meteorological parameters that cannot be easily obtained in many regions, other simple models along with the soft computing models are used to obtain ET o values. The paper presents a comprehensive comparison of 12 soft computing models: gene expression programming (GEP), neuro‐fuzzy with grid partitioning (NF‐GP), neuro‐fuzzy with sub‐clustering (NF‐SC), multivariate adaptive regression spline (MARS), boosted regression tree (BT), random forest (RF), model tree (MT), support vector machine (SVM), SVM‐firefly algorithm (SVM‐FA), extreme learning machine (ELM), neural network‐particle swarm optimization (NN‐PSO) and neural network‐differential evolution (NN‐DE), to estimate daily ET o values in humid regions. Therefore, daily meteorological data from two weather stations (during a 12 year period) were used to assess the models. The obtained results revealed that very good efficiency was obtained from all the applied methods. The temperature‐based SVM‐FA (root mean square error (RMSE) = 0.324 mm, Nash–Sutcliffe co‐efficient (NS) = 0.960) and NF‐GP (RMSE = 0.272 mm, NS = 0.974) models generally provided the best accuracy when estimating the ET o of Sari and Bablosar, two humid stations in northern Iran, respectively. The accuracy ranks of the other models (from best to worst) were found to be: NN‐PSO, NF‐SC, ELM, NN‐DE, MARS, GEP, RF, SVM, BT and MT. Among the radiation‐based models, the NF‐GP provided the best accuracy at estimating the ET o of both stations. The other models were ranked as: ELM, SVM‐FA, NN‐DE, NN‐PSO, MARS, RF, BT, NF‐SC, SVM and MT, respectively.