Montana flume aeration performance evaluation with machine learning models (Record no. 20425)

000 -LEADER
fixed length control field a
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20231222104108.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231222b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency AIKTC-KRRC
Transcribing agency AIKTC-KRRC
100 ## - MAIN ENTRY--PERSONAL NAME
9 (RLIN) 22563
Author Tiwari, Ashwini
245 ## - TITLE STATEMENT
Title Montana flume aeration performance evaluation with machine learning models
250 ## - EDITION STATEMENT
Volume, Issue number Vol.104(1), Mar
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. USA
Name of publisher, distributor, etc. Springer
Year 2023
300 ## - PHYSICAL DESCRIPTION
Pagination 175-186p.
520 ## - SUMMARY, ETC.
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
9 (RLIN) 4642
Topical term or geographic name entry element Humanities and Applied Sciences
700 ## - ADDED ENTRY--PERSONAL NAME
9 (RLIN) 22564
Co-Author Ojha, C. S. P.
773 0# - HOST ITEM ENTRY
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)
Source of classification or shelving scheme
Koha item type Articles Abstract Database
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Barcode Date last seen Price effective from Koha item type
          School of Engineering & Technology School of Engineering & Technology Archieval Section 2023-12-22 2023-1766 2023-12-22 2023-12-22 Articles Abstract Database
Unique Visitors hit counter Total Page Views free counter
Implemented and Maintained by AIKTC-KRRC (Central Library).
For any Suggestions/Query Contact to library or Email: librarian@aiktc.ac.in | Ph:+91 22 27481247
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha