Prediction of maximum scour depth at clear water conditions: Multivariate and robust comparative analysis between empirical equations and machine learning approaches using extensive reference metadata

Buddhadev Nandi; Gaurav Patel; Subhasish Das

doi:10.1016/j.jenvman.2024.120349

Prediction of maximum scour depth at clear water conditions: Multivariate and robust comparative analysis between empirical equations and machine learning approaches using extensive reference metadata

J Environ Manage. 2024 Mar:354:120349. doi: 10.1016/j.jenvman.2024.120349. Epub 2024 Feb 24.

Authors

Buddhadev Nandi¹, Gaurav Patel², Subhasish Das³

Affiliations

¹ School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India. Electronic address: buddhadevn.swre.rs@jadavpuruniversity.in.
² School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India. Electronic address: gauravp.wre.rs@jadavpuruniversity.in.
³ School of Water Resources Engineering, Jadavpur University, Kolkata 700032, India. Electronic address: subhasish.das@jadavpuruniversity.in.

PMID: 38401497
DOI: 10.1016/j.jenvman.2024.120349

Abstract

Flow obstructed by bridge piers can increase sediment transport leading to local scour. This local scour poses a risk to the stability of bridge structures, which could lead to structural failures. There are two main approaches for evaluating the scour depth (d_s) of bridge piers. The first is based on understanding hydraulic phenomena and developing relationships with properties affecting scour. The second uses data-driven soft computing models that lack physical interpretations but rely on algorithms to predict outcomes. Methods are chosen by researchers based on their goals and resources. This study aims to create innovative ensemble frameworks comprising support vector machine for regression (SVMR), random forest regression (RFR), and reduced error pruning tree (REPTree) as base learners, alongside bagging regression tree (BRT) and stochastic gradient boosting (SGB) as meta learners. These ensembles were developed to analyse maximum scour depths (d_sm) in clear water conditions, utilizing 35 literature's experimental data published in last 63 years. The performance of each machine learning (ML) approach was assessed using statistical performance indicators. The proposed model was also compared with top six empirical equations with strong predictive ability. Results show that among these empirical equations, the equation from Nandi and Das (2023) performs best. Performance evaluation considering training, testing, and the entire dataset, SGB (REPTree), BRT(SVMR-PUK), and SGB (REPTree) exhibited the highest performance, securing the top rank among all ML models and empirical equations. Sensitivity analysis identified sediment gradation and flow intensity as the most influential variables for predicting d_sm during both training and testing phases, respectively.

Keywords: Bagging; Boosting; Clear water scour; Machine learning; Random forest; Support vector regression.

MeSH terms

Algorithms
Machine Learning
Metadata*
Water*

Substances

Water