Radial basis function artificial neural network (rbfann) model for simulating daily runoff from the himalayan watersheds (Record no. 19138)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230405130609.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230405b 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 | Radial basis function artificial neural network (rbfann) model for simulating daily runoff from the himalayan watersheds |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.41(1), Jan |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Roorkee |
| Name of publisher, distributor, etc. | Indian Water Resources Society |
| Year | 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 41-53p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | In this paper, a Radial Basis Function Artificial Neural Network (RBFANN) model was developed based on k-means clustering algorithm to<br/>simulate the daily rainfall-runoff process in three Himalayan watersheds i.e., Naula, Chaukhutia, and Ramganga located in Uttarakhand<br/>State, India. Different network parameters such as learning rate in the function layer (ALR), learning rate in output layer (ALRG), and the<br/>number of iterations were optimized. The outcomes of the RBFANN model was evaluated by using statistical (i.e., root mean square error:<br/>RMSE, correlation coefficient: CC, and Nash-Sutcliffe efficiency: NSE) and hydrological (i.e., volumetric error: EV) indicators during<br/>calibration, cross-validation, and validation phases. The performance of the RBFANN model improved and stabilized within 500 iterations.<br/>The model was very sensitive to learning rate in the function layer (ALR), however, not in the output layer (ALRG). Overall results reveal a<br/>promising performance of the RBFANN model in simulating the daily runoff in the study catchments. |
| 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) | 20413 |
| Co-Author | Kasiviswanathan, K. S. |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0970-6984 |
| Title | Journal of indian water resource society |
| Place, publisher, and date of publication | Roorkee Indian Institute of Technology Roorkee |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://iwrs.org.in/journal/jan2021/6jan.pdf |
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 05/04/2023 | 2023-0622 | 05/04/2023 | 05/04/2023 | Articles Abstract Database |