AUTHOR=Habib M. A. , Abolfathi S. , O’Sullivan John. J. , Salauddin M. 

TITLE=Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

JOURNAL=Frontiers in Built Environment

VOLUME=Volume 10 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/built-environment/articles/10.3389/fbuil.2024.1343398

DOI=10.3389/fbuil.2024.1343398

ISSN=2297-3362

ABSTRACT=Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient the good design and ongoing maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence in the form ofand Machine Learning (ML) algorithms has have gained interestdeveloped significantly, and although they now underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive modelsThe performance of the models is validated against extensive laboratory-based physical modelling data. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre-and post-processing steps of ML-based models. In-depth statistical analyses are conducted proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions overall with a Coefficient of Determination (r 2 ) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.