Montana flume aeration performance evaluation with machine learning models
By: Tiwari, Ashwini
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Contributor(s): Ojha, C. S. P.
Publisher: USA Springer 2023Edition: Vol.104(1), Mar.Description: 175-186p.Subject(s): Humanities and Applied SciencesOnline resources: Click here
In:
Journal of the institution of engineers (India): Series ASummary: Montana flume is derived from Parshall flume by eliminating diverging part and throat. The mass transfer of oxygen from the atmosphere into the water is known as aeration. The dissolved oxygen (D.O.) concentration in the water body determines water quality. The experiment was performed on six different Montana flumes fixed in a tilting prismatic rectangular channel. Experimental observations were used to develop classical and machine learning models to predict Montana flume aeration efficiency. The developed models are namely multi nonlinear regression (MNLR), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN). The models were tested, and the results show that all these three developed models perform very well. However, ANN gives better results than other models as it has the highest cc and lowest rmse values. According to the sensitivity analysis results, the Reynolds number (Re) was the most crucial input element in determining the aeration efficiency of the Montana flume in the case of dimensionless datasets. However, discharge per unit width (q) is found to be of relative significance in the case of dimensional datasets.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-1766 |
Montana flume is derived from Parshall flume by eliminating diverging part and throat. The mass transfer of oxygen from the atmosphere into the water is known as aeration. The dissolved oxygen (D.O.) concentration in the water body determines water quality. The experiment was performed on six different Montana flumes fixed in a tilting prismatic rectangular channel. Experimental observations were used to develop classical and machine learning models to predict Montana flume aeration efficiency. The developed models are namely multi nonlinear regression (MNLR), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN). The models were tested, and the results show that all these three developed models perform very well. However, ANN gives better results than other models as it has the highest cc and lowest rmse values. According to the sensitivity analysis results, the Reynolds number (Re) was the most crucial input element in determining the aeration efficiency of the Montana flume in the case of dimensionless datasets. However, discharge per unit width (q) is found to be of relative significance in the case of dimensional datasets.
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