The choice of input variables is a fundamental, and yet crucial consideration in identifying
the optimal functional form of statistical models. The task of selecting input variables is …

In machine learning, one of the main requirements is to build computational models with a
high ability to generalize well the extracted knowledge. When training eg artificial neural …

Data splitting is an important consideration during artificial neural network (ANN)
development where hold-out cross-validation is commonly employed to ensure …

Artificial neural networks (ANNs) have been widely used to model environmental processes.
The ability of ANN models to accurately represent the complex, non-linear behaviour of …

Recent trends in the management of water supply have increased the need for modelling
techniques that can provide reliable, efficient, and accurate representation of the complex …

Urbanization induces detrimental effects on the eco-environment that are beginning to be
extensively described. However, the adoption of suitable indicators and reliable methods …

Seven state-of-the-art machine learning techniques for estimation of construction costs of
reinforced-concrete and prestressed concrete bridges are investigated in this paper …

Data splitting is an important step in the artificial neural network (ANN) development
process, whereby the available data are divided into training, testing, and validation subsets …

CO 2 capture techniques are being developed faster by develo** models that predict the
solubility of CO 2 in various solvents. Artificial neural network (ANN) model is developed in …

The paper presents and analyzes the state-of-the-art machine learning techniques that can
be applied as a decision-support system in the estimation of resource consumption in the …