Various forms of machine learning (ML) methods have historically played a valuable role in
environmental remote sensing research. With an increasing amount of “big data” from earth …

Reference Evapotranspiration (ET o) is a complex, dynamic and non-linear hydrological
process. Accurate estimation of ET o has long been an eminent topic of interest in the …

Hybrid heuristic algorithm (HA), an innovative technique in the machine learning field,
enhances the accuracy of reference evapotranspiration (ETo) prediction, which is of …

Accurate estimation of reference evapotranspiration (ET 0) is very important in hydrological
cycle research, and is essential in agricultural water management and allocation. The …

Accurate estimation of reference evapotranspiration (ET 0) is needed for planning and
managing water resources and agricultural production. The FAO-56 Penman–Monteith …

With the limited availability of meteorological variables in many remote areas, estimation of
evapotranspiration (ET) at different spatiotemporal scales for efficient irrigation water …

The use of artificial neural networks (ANNs) in estimation of evapotranspiration has received
enormous interest in the present decade. Several methodologies have been reported in the …

Evapotranspiration (ET) is a major component of the hydrologic cycle and its accurate
forecasting is essential in all water resources applications. In this study, artificial neural …

Precise estimation of evapotranspiration is crucial for accurate crop-water estimation.
Recently machine learning (ML) techniques like artificial neural network (ANN) are being …

The major problem when dealing with modeling evapotranspiration process is its nonlinear
dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) …