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|Title:||Machine learning model development for predicting aeration efficiency through Parshall flume||Authors:||Sangeeta
Haji Seyed Asadollah, SB
|Issue Date:||2021||Source:||Engineering applications of computational fluid mechanics, 2021, v. 15, no. 1, p. 889-901||Abstract:||This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E20) through the Parshall flume. The required dataset is obtained from the laboratory tests using different flumes fabricated in National Institute Technology Kurukshetra, India. Besides, the potential of K Nearest Neighbor (KNN), Random Forest Regression(RFR), and Decision Tree Regression (DTR) models are evaluated to predict the aeration efficiency. In this way, several input combinations (e.g. M1-M15) are provided using the laboratory parameters (e.g. W/L, S/L, Fr, and Re). Different predictive models are obtained based on those input combinations and machine learning models proposed in the present study. The predictive models are assessed based on several performance metrics and visual indicators. Results show that the KNN-M11 model (RMSEtesting = 0.002, R2 testing = 0.929), which includes W/L, S/L, and Fr as predictive variables outperforms the other predictive models. Furthermore, an enhancement is observed in KNN model estimation accuracy compared to the previously developed empirical models. In general, the predictive model dominated in the present study provides adequate performance in predicting the aeration efficiency in the Parshall flume.||Keywords:||Aeration efficiency
Machine learning models
|Publisher:||Hong Kong Polytechnic University, Department of Civil and Structural Engineering||Journal:||Engineering applications of computational fluid mechanics||ISSN:||1994-2060||EISSN:||1997-003X||DOI:||10.1080/19942060.2021.1922314||Rights:||© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The following publication Sangeeta, Haji Seyed Asadollah, S. B., Sharafati, A., Sihag, P., Al-Ansari, N., & Chau, K. W. (2021). Machine learning model development for predicting aeration efficiency through Parshall flume. Engineering Applications of Computational Fluid Mechanics, 15(1), 889-901 is available at https://doi.org/10.1080/19942060.2021.1922314
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