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

Engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Chou, Jui-Sheng Molla, Asmare Department of Civil and Construction Engineering National Taiwan University of Science and Technology

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 MAPE and R values of 20.92% and 0.9435, respectively, and for scour depth data at complex pier foundations, it records MAPE and R values of 6.49% and 0.9384, respectively. The automated predictive analytics underscore the robustness, efficiency, and stability of ATO-WFLSSVR compared to existing methods. This study's notable contributions include the development of an innovative optimization algorithm named Arctic Terns Optimizer (ATO), proficiency in solving high-dimensional optimization problems, and the creation of a user-friendly graphical interface system, a promising tool for civil engineers to estimate scour depth at bridges. Further testing and evaluation of ATO-WFLSSVR across diverse datasets encompassing more complex scenarios are recommended. The data and source code for this study are currently accessible at https://***/profile/Jui-Sheng-Chou/publications. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis

关键词： Artic Tern Optimizer (ATO) least squares support vector regression machine learning metaheuristic algorithm Scour depth at bridges tuning hyperparameter in machine learning weighted feature-based method

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