Under changing climate and burgeoning food production demands, climate-smart
agriculture (CSA) practices are the need of the hour. Physically-based crop models have …

Difficulties are faced when formulating hydrological processes, including that of
evapotranspiration (ET). Conventional empirical methods for formulating these possess …

Evaporation is an important hydrological process in the water cycle, especially for water
bodies. Machine Learning (ML) models have become accurate and powerful tools in …

In achieving water resource management goals such as irrigation scheduling, an accurate
estimate of reference evapotranspiration (ET 0) is critical. Support vector regression (SVR) …

Computation of reference evapotranspiration (ETo) poses a challenge under limited
meteorological data availability. However, even in this case, hourly data may be available …

In this research, monthly wind speed time series of the Kirsehir was investigated using the
stand-alone, hybrid and ensemble models. The artificial neural networks, Gaussian process …

In recent decades, various conventional techniques have been formulated around the world
to evaluate the overall water quality (WQ) at particular locations. In the present study, back …

For developing countries, scarcity of climatic data is the biggest challenge, and model
development with limited meteorological input is of critical importance. In this study, five data …

In this study, the predictive power of three different machine learning (ML)-based
approaches, namely, multi-gene genetic programming (MGGP), M5 model trees (M5Tree) …

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 …