| 000 -LEADER |
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| fixed length control field |
a |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20231222104108.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
231222b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
AIKTC-KRRC |
| Transcribing agency |
AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22563 |
| Author |
Tiwari, Ashwini |
| 245 ## - TITLE STATEMENT |
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| Title |
Montana flume aeration performance evaluation with machine learning models |
| 250 ## - EDITION STATEMENT |
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| Volume, Issue number |
Vol.104(1), Mar |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
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| Place of publication, distribution, etc. |
USA |
| Name of publisher, distributor, etc. |
Springer |
| Year |
2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Pagination |
175-186p. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| 9 (RLIN) |
4642 |
| Topical term or geographic name entry element |
Humanities and Applied Sciences |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
22564 |
| Co-Author |
Ojha, C. S. P. |
| 773 0# - HOST ITEM ENTRY |
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| International Standard Serial Number |
2250-2149 |
| Place, publisher, and date of publication |
Switzerland Springer |
| Title |
Journal of the institution of engineers (India): Series A |
| 856 ## - ELECTRONIC LOCATION AND ACCESS |
|---|
| URL |
https://link.springer.com/article/10.1007/s40030-022-00706-5 |
| Link text |
Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
|
| Koha item type |
Articles Abstract Database |
