Machine learning model development for predicting aeration efficiency through Parshall flume

Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


This study compares several advanced machine learning models to obtain the most accurate method for predicting the aeration efficiency (E 20) 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 ((Formula presented.)), 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.

Original languageEnglish
Pages (from-to)889-901
Number of pages13
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - May 2021


  • Aeration efficiency
  • Parshall flume
  • machine learning models
  • prediction

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation


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