This paper presents a comprehensive review of machine learning (ML) based approaches,
especially artificial neural networks (ANNs) in time series data prediction problems …

Abstract Machine learning (ML) plays an essential role in various scientific fields. ML
streamlines renewable energy systems, boosting efficiency and production, as global …

The shortage of available and suitable construction sites in city centres has led to the
increased use of problematic areas, where the bearing capacity of the underlying deposits is …

When it comes to the expansion of the renewable energy business in today technological
age, the ability to predict power and energy output based on shifting weather patterns is …

Boundary shear stress distribution of a compound channel is generally influenced by the
geometric, roughness, and hydraulic parameters. Experiments are performed on both …

Apart from the vertical axial loads, the footings of portal-framed buildings are often subjected
to eccentric and eccentric-inclined loads caused by forces of earth pressures, earthquakes …

This study, which aims to make predictions using a previously unused deep hybrid
Convolutional Neural Network-Support Vector Regression approach for hydropower …

Estimating the accurate longitudinal velocity fields in an open channel junction has a great
impact on hydraulic structures such as irrigation and drainage channels, river systems and …

In operating a complex hydroelectric system in a competitive market the operational as well
as the financial risks are high. Decision makers and operators unarmed with rigorous …

Accurate energy production forecasting is critical when planning energy for the economic
development of a country. A deep learning approach based on Long Short-Term Memory …