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

• Main Authors: Chou, Jui-Sheng, Molla, Asmare
• Format: Dataset
• Language: unknown
• Published: Taylor & Francis 2024
• Subjects: Space Science; Biotechnology; Biological Sciences not elsewhere classified; Information Systems not elsewhere classified; Cancer; Arctic tern
• Online Access: https://dx.doi.org/10.6084/m9.figshare.26206556; https://tandf.figshare.com/articles/dataset/Arctic_tern-optimized_weighted_feature_regression_system_for_predicting_bridge_scour_depth/26206556

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 ...