Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aest...
Accurate prediction of the reference evapotranspiration (ET) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinatio...
Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E) are particularly complex, yet are often assumed to...
Reference evapotranspiration ( ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for estimation for specific combinations of available meteorological p...
The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining ...
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agr...
Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ETo). Efforts have been made to simplify the (ETo) estimation using machine learning models. The existing approaches are limited to a single ...
Reference evapotranspiration (ETo) is essential for agricultural water management, crop productivity, and irrigation systems. The Penman-Monteith (PM) equation is the standard method for estimating ETo, but its data-intensive nature makes it impracti...