Data-Based Evapotranspiration Modeling

This chapter focuses on data-based modeling of evapotranspiration and evaporation from three totally different eco-climatic regions. In the first few sections, data-based modeling (artificial neural network) results are compared with reference to evapotranspiration (ET0), estimated using traditional models from meteorological data. The second section is fully dedicated to evaporation modeling with data-based modeling concepts and input section procedures applied to evaporation modeling. In Sect. 7.1, we describe the mathematical details of the reference evapotranspiration models used. Analyses with traditional reference evapotranspiration models are performed on data from the Brue catchment, UK and the Santa Monica Station, USA. In Sect. 7.2, studies are described which have been conducted to see how data selection approaches respond to the evaporation data from the Chahnimeh reservoirs region in Iran. In this case study, we consider comprehensive use of data selection approaches and machine learning AI approaches. We have employed different model selection approaches such as GT, AIC, BIC, entropy theory (ET), and traditional approaches such as data splitting and cross correlation method on this daily evaporation data. Modeling with conventional models and hybrid wavelet based models was performed as per recommendations.