Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …
leading to loss of life, damage to properties, and economic disruption. Landslide …
Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
Contributions and risks of artificial intelligence (AI) in building smarter cities: Insights from a systematic review of the literature
Artificial intelligence (AI) is one of the most disruptive technologies of our time. Interest in the
use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban …
use of AI for urban innovation continues to grow. Particularly, the rise of smart cities—urban …
Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These
landslides can be deadly to the community living downslope with their fast pace, turning …
landslides can be deadly to the community living downslope with their fast pace, turning …
[HTML][HTML] A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide …
Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …
tremendous success. However, researchers and practitioners still face challenges in …
A novel integrated approach of augmented grey wolf optimizer and ANN for estimating axial load carrying-capacity of concrete-filled steel tube columns
The purpose of this study is to offer a high-performance machine learning model for
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
determining the ultimate load-carrying capability of concrete-filled steel tube (CFST) …
A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model
Recycled aggregate concrete is used as an alternative material in construction engineering,
aiming to environmental protection and sustainable development. However, the …
aiming to environmental protection and sustainable development. However, the …
Employing a genetic algorithm and grey wolf optimizer for optimizing RF models to evaluate soil liquefaction potential
Among the research hotspots in geological/geotechnical engineering, research on the
prediction of soil liquefaction potential is still limited. In this research, several machine …
prediction of soil liquefaction potential is still limited. In this research, several machine …
Application of a hybrid artificial neural network-particle swarm optimization (ANN-PSO) model in behavior prediction of channel shear connectors embedded in normal …
Featured Application Behavior prediction of channel shear connectors in normal and high-
strength concrete (HSC) without conducting costly experiments. Abstract Channel shear …
strength concrete (HSC) without conducting costly experiments. Abstract Channel shear …
Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques
The quality of digital elevation models (DEMs), as well as their spatial resolution, are
important issues in geomorphic studies. However, their influence on landslide susceptibility …
important issues in geomorphic studies. However, their influence on landslide susceptibility …