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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Taylor & Francis
2024
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ftdatacite:10.6084/m9.figshare.26206556.v1 2024-09-15T17:54:26+00:00 Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... Chou, Jui-Sheng Molla, Asmare 2024 https://dx.doi.org/10.6084/m9.figshare.26206556.v1 https://tandf.figshare.com/articles/dataset/Arctic_tern-optimized_weighted_feature_regression_system_for_predicting_bridge_scour_depth/26206556/1 unknown Taylor & Francis https://dx.doi.org/10.6084/m9.figshare.26206556 https://dx.doi.org/10.1080/19942060.2024.2364745 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Space Science Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer dataset Dataset 2024 ftdatacite https://doi.org/10.6084/m9.figshare.26206556.v110.6084/m9.figshare.2620655610.1080/19942060.2024.2364745 2024-08-01T09:44:27Z 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 hybrid models by uniting a metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The metaheuristic algorithm concurrently optimizes all hyperparameters of constituent WFLSSVR models, resulting in a highly effective system. Validation involves a comprehensive assessment using two case studies, which include datasets of scour depths across various complexities and pier foundation types. Comparative analyses against single AI models, conventional ensemble models, hybrid techniques, and empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others in performance evaluation metrics. Specifically, for the field dataset, ATO-WFLSSVR achieves ... Dataset Arctic tern DataCite |
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Open Polar |
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ftdatacite |
language |
unknown |
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Space Science Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer |
spellingShingle |
Space Science Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Chou, Jui-Sheng Molla, Asmare Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
topic_facet |
Space Science Biotechnology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer |
description |
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 hybrid models by uniting a metaheuristic optimization algorithm with weighted features and least squares support vector regression (WFLSSVR). The metaheuristic algorithm concurrently optimizes all hyperparameters of constituent WFLSSVR models, resulting in a highly effective system. Validation involves a comprehensive assessment using two case studies, which include datasets of scour depths across various complexities and pier foundation types. Comparative analyses against single AI models, conventional ensemble models, hybrid techniques, and empirical methods demonstrate that ATO-WFLSSVR's reliability outperforms others in performance evaluation metrics. Specifically, for the field dataset, ATO-WFLSSVR achieves ... |
format |
Dataset |
author |
Chou, Jui-Sheng Molla, Asmare |
author_facet |
Chou, Jui-Sheng Molla, Asmare |
author_sort |
Chou, Jui-Sheng |
title |
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
title_short |
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
title_full |
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
title_fullStr |
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
title_full_unstemmed |
Arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
title_sort |
arctic tern-optimized weighted feature regression system for predicting bridge scour depth ... |
publisher |
Taylor & Francis |
publishDate |
2024 |
url |
https://dx.doi.org/10.6084/m9.figshare.26206556.v1 https://tandf.figshare.com/articles/dataset/Arctic_tern-optimized_weighted_feature_regression_system_for_predicting_bridge_scour_depth/26206556/1 |
genre |
Arctic tern |
genre_facet |
Arctic tern |
op_relation |
https://dx.doi.org/10.6084/m9.figshare.26206556 https://dx.doi.org/10.1080/19942060.2024.2364745 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.6084/m9.figshare.26206556.v110.6084/m9.figshare.2620655610.1080/19942060.2024.2364745 |
_version_ |
1810430756033396736 |