Proper irrigation scheduling and agricultural water management require a precise
estimation of crop water requirement. In practice, reference evapotranspiration (ETo) is firstly …

The increasing frequency of droughts and floods due to climate change has severely
affected water resources across the globe in recent years. An optimal design for the …

The potential of generalized deep learning models developed for crop water estimation was
examined in the current study. This study was conducted in a semiarid region of India, ie …

Accurate estimation of reference evapotranspiration (ET 0) values is of crucial importance in
hydrology, agriculture and agro-meteorology issues. The present study reports a …

Accurate estimation of reference evapotranspiration (ET o) is profoundly crucial in crop
modeling, sustainable management, hydrological water simulation, and irrigation …

The present study investigates and evaluate the scope and potential of modern computing
tools and techniques such as ensembled machine learning methods in estimating ETo. Five …

Evapotranspiration (ET) is considered as a key factor in hydrological and climatological
studies, agricultural water management, irrigation scheduling, etc. It can be directly …

In this research, five hybrid novel machine learning approaches, artificial neural network
(ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO) …

Actual evapotranspiration (AET) is one of the decisive factors controlling the water balance
at the catchment level, particularly in arid and semi-arid regions, but measured data for …

To accurately manage water resources, a precise prediction of reference evapotranspiration
(ET ref) is necessary. The best empirical equations to determine ET ref are usually the …