This study compares the ability of Long Short-Term Memory (LSTM) tuned with Grey Wolf
Optimization (GWO) and machine learning models, artificial neural network (ANN), Adaptive …

For the first time, a novel hybrid machine learning model named the least-squares support
vector machine-arithmetic optimization algorithm (LSSVM-AOA) was proposed. The …

Uncertainty quantification plays a crucial role in the design, monitoring, and risk assessment
of earth dams. To reduce the computational burden, we employ a combination of finite …

This study aims to develop machine learning-based prediction models for the out-of-plane
displacement of infill walls under earthquake excitation by using machine learning methods …

The service life performance of conventional and modified concrete subjected to harsh
climatic condition environment is directly related to durability properties of concrete like …

Fast multi-output relevance vector regression (FMRVR) algorithm is developed for
simultaneous estimation of groundwater and lake water depth for the first time in this study …

Abrupt changes in the Lake Urmia water level have been addressed in many studies, and
yet the link between the water level decline and hydro‐meteorological variables in the basin …

In this paper, we consider the Vennia algorithm to conduct in‐depth research and analysis
on the traceability of dual‐chain blockchain agricultural products' E‐commerce information …

Bearing diagnosis is important to ensure smooth machinery operation and safety. Machine
learning methods have been used widely in bearing diagnosis study, and one of the recent …

Lakes, as the main sources of surface water, are of great environmental and ecological
importance and largely affect the climatic conditions of the surrounding areas. Lake area …