Arctic tern-optimized weighted feature regression system for predicting bridge scour depth

Arctic tern-optimized weighted feature regression system for predicting bridge scour depth

This paper presents a pioneering artificial intelligence (AI) solution – the Arctic Tern-Optimized Weighted Feature Least Squares Support Vector Regression (ATO-WFLSSVR) system to aid civil engineers in accurately predicting scour depth at bridges. This prediction system amalgamates the strengths of...

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Bibliographic Details
Main Authors: Jui-Sheng Chou, Asmare Molla
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:Engineering Applications of Computational Fluid Mechanics
Subjects:
Scour depth at bridges
metaheuristic algorithm
Artic Tern Optimizer (ATO)
machine learning
weighted feature-based method
least squares support vector regression
Online Access:https://www.tandfonline.com/doi/10.1080/19942060.2024.2364745
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https://www.tandfonline.com/doi/10.1080/19942060.2024.2364745

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