000			a
999			_c20425 _d20425
003			OSt
005			20231222104108.0
008			231222b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_922563 _aTiwari, Ashwini
245			_aMontana flume aeration performance evaluation with machine learning models
250			_aVol.104(1), Mar
260			_aUSA _bSpringer _c2023
300			_a175-186p.
520			_aMontana 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.
650		0	_94642 _aHumanities and Applied Sciences
700			_922564 _aOjha, C. S. P.
773	0		_x 2250-2149 _dSwitzerland Springer _tJournal of the institution of engineers (India): Series A
856			_uhttps://link.springer.com/article/10.1007/s40030-022-00706-5 _yClick here
942			_2ddc _cAR