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Role of Regression Models in Bridge Pier Scour Prediction

Role of Regression Models in Bridge Pier Scour Prediction

Roshni Thendiyath, Vijay Prakash
Copyright: © 2020 |Volume: 11 |Issue: 2 |Pages: 15
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802853|DOI: 10.4018/IJAMC.2020040108
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MLA

Thendiyath, Roshni, and Vijay Prakash. "Role of Regression Models in Bridge Pier Scour Prediction." IJAMC vol.11, no.2 2020: pp.156-170. http://doi.org/10.4018/IJAMC.2020040108

APA

Thendiyath, R. & Prakash, V. (2020). Role of Regression Models in Bridge Pier Scour Prediction. International Journal of Applied Metaheuristic Computing (IJAMC), 11(2), 156-170. http://doi.org/10.4018/IJAMC.2020040108

Chicago

Thendiyath, Roshni, and Vijay Prakash. "Role of Regression Models in Bridge Pier Scour Prediction," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.2: 156-170. http://doi.org/10.4018/IJAMC.2020040108

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Abstract

Scour monitoring is an important concern in the design of any hydraulic structure. This study introduces the application of regression models in the prediction of scour depth around a bridge pier. Feedforward Neural Network (FFNN) and Multivariate Adaptive Regression Spline (MARS) models have been developed using different flow parameters. The flow parameters taken into consideration are the flow depth, flow velocity, pier diameter, and Froude's number. The FFNN models with different combinations of input parameters along with a simultaneous variation in the number of hidden neurons were developed to further increase the prediction accuracy. The best combination of hidden neurons and input parameters was selected and compared with the developed MARS model. Further, these models were compared with the selected empirical models to find out the best possible model for bridge pier scour prediction. All the developed regression models and selected empirical models were compared using standard statistical performance evaluation measures such as Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), Mean Absolute Percentage Error (MAPE) and Percentage BIAS (PBIAS). The FFNN model developed with 4-input parameters performed better compared with other combinations of input parameters. The performance indices of all developed models show that as the input parameter increases, prediction accuracy also increases. A superior prediction accuracy was observed with FFNN model with 4-input parameters compared to MARS model and other selected empirical models.

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