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 …

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

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

Reference evapotranspiration (ET0) is one of the crucial variables used for irrigation
scheduling, agricultural production, and water balance studies. This study compares six …

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water
resources management, the accurate estimation of reference evapotranspiration (ET o) is …

In the current study, an ability of a novel regression-based method is evaluated in modeling
daily reference evapotranspiration (ET0), which is an important issue in water resources …

The study investigates the potential of two new machine learning methods, least-square
support vector regression with a gravitational search algorithm (LSSVR-GSA) and the …

The precision of reference evapotranspiration (ETo) predictions would vary, depending on
the adopted empirical method and the availability of meteorological data. This study aims to …

Accurate estimation of suspended sediment (SS) is very essential for planning and
management of hydraulic structures. The study investigates the accuracy of four machine …

Applying the IoT paradigm in agriculture generates an unprecedented amount of data about
fields and crops worldwide. With data, machine learning is natural to forecast or estimate …