MODELLING OF SPRING FLOW USING ARTIFICIAL NEURAL NETWORK (Record no. 15360)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20211013110831.0 |
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| fixed length control field | 211008b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 14482 |
| Author | Rawat, S. S. |
| 245 ## - TITLE STATEMENT | |
| Title | MODELLING OF SPRING FLOW USING ARTIFICIAL NEURAL NETWORK |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol,39(3), July |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Roorkee |
| Name of publisher, distributor, etc. | Indian Water Resources Society |
| Year | 2019 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 10-17p |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | n the mountainous region, springs are the main sources of drinking water supply as well as domestic water, therefore these s<br/>of the region. During summer, spring flow reduces significantly and sometimes they dried<br/>hydrology is complex in nature and lack of a database on springs restricts the application of developed hydrological models <br/>critical waters resources as a sustainable source of water in the re<br/>developed to predict the spring flow for two springs namely, Hill Campus and Fakua from Tehri Garhwal district of Uttarakhand<br/>data of precipitation (P), temperature (T), relative humidity (RH) and spring flow (Q) from the year 1999 to the year 2003 were used to <br/>model the spring flow. First, four-year data was used to train the models, whereas, remaining one<br/>models in predicting the springflow. Seven models differ in input parameters (P, T, RH & Q) have been developed and the best fit model was <br/>selected on the basis of capability of the network to converge normalize system error (NSE). Developed models performance wer<br/>by categorize all data samples into six ranges (0<br/>with observed data. A close agreement between observed and ANN predicted spring flow in the testing period for both the sprin<br/>the capability of ANN in predicting the spring flow even in data scare situation. The study also reveals that variability in <br/>results in better accuracy in the testing period |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4621 |
| Topical term or geographic name entry element | Civil Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 14487 |
| Co-Author | Mathur, Sanjay |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0970-6984 |
| Place, publisher, and date of publication | Roorkee Indian Institute of Technology Roorkee |
| Title | Journal of indian water resource society |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | http://iwrs.org.in/39-3/ |
| Link text | Click Here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 08/10/2021 | 2021-2022200 | 08/10/2021 | 08/10/2021 | Articles Abstract Database |