Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
A systematic review of applications of machine learning techniques for wildfire management decision support
Wildfires threaten and kill people, destroy urban and rural property, degrade air quality,
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
ravage forest ecosystems, and contribute to global warming. Wildfire management decision …
[HTML][HTML] Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques
A reliable and accurate prediction of the tunnel boring machine (TBM) performance can
assist in minimizing the relevant risks of high capital costs and in scheduling tunneling …
assist in minimizing the relevant risks of high capital costs and in scheduling tunneling …
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Using multiple ensemble learning techniques for improving the predictive accuracy of
landslide models is an active research area. In this study, we combined a radial basis …
landslide models is an active research area. In this study, we combined a radial basis …
Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method
Forest fire risk has increased globally during the previous decades. The Mediterranean
region is traditionally the most at risk in Europe, but continental countries like Serbia have …
region is traditionally the most at risk in Europe, but continental countries like Serbia have …
Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models
HB Ly, BT Pham, LM Le, TT Le, VM Le… - Neural Computing and …, 2021 - Springer
The main objective of the present work is to estimate the load-carrying capacity of concrete-
filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) …
filled steel tubes (CFST) under axial compression using hybrid artificial intelligence (AI) …
Flood susceptible prediction through the use of geospatial variables and machine learning methods
NM Gharakhanlou, L Perez - Journal of hydrology, 2023 - Elsevier
Floods are one of the most perilous natural calamities that cause property destruction and
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
endanger human life. The spatial patterns of flood susceptibility were assessed in this study …
Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm
Fire is among the most dangerous and devastating natural hazards in forest ecosystems
around the world. The development of computational ensemble models for improving the …
around the world. The development of computational ensemble models for improving the …
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing
catastrophic losses. These losses have underscored the urgent need to improve public …
catastrophic losses. These losses have underscored the urgent need to improve public …
[HTML][HTML] Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques
BT Pham, A Jaafari, T Van Phong, HPH Yen… - Geoscience …, 2021 - Elsevier
Improving the accuracy of flood prediction and mapping is crucial for reducing damage
resulting from flood events. In this study, we proposed and validated three ensemble models …
resulting from flood events. In this study, we proposed and validated three ensemble models …