Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

In this paper, we provide a review of the research dedicated to applications of data science
techniques, and especially machine learning techniques, in relevant agricultural systems …

In the current study, the ability of three data-driven methods of Gene Expression
Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were …

Dew point temperature (DPT) is known to fluctuate in space and time regardless of the
climatic zone considered. The accurate estimation of the DPT is highly significant for various …

The likelihood of surface water and groundwater contamination is higher in regions close to
landfills due to the possibility of leachate percolation, which is a potential source of pollution …

Capabilities of the bat algorithm optimized extreme learning machine (Bat-ELM) model for
dew point temperature (T dew) estimation were evaluated in this study, in comparison with …

Dew point temperature (T dew) plays an important role in hydrology, meteorology, and other
related research. This study evaluated the ability of a new machine learning model (hybrid …

Streamflow forecasting can offer valuable information for optimal management of water
resources, flood mitigation, and drought warning. This research aims in evaluating the …

The potential of different models–deep echo state network (DeepESN), extreme learning
machine (ELM), extra tree (ET), and regression tree (RT)–in estimating dew point …

Accurate estimation of dew point temperature (Tdew) has a crucial role in sustainable water
resource management. This study investigates kernel extreme learning machine (KELM) …