FLOOD FORECASTING USING HYBRID WAVELET NEURAL NETWORK MODEL (Record no. 13703)
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| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20201120144301.0 |
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| fixed length control field | 201120b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 12639 |
| Author | Venkataramana, R. |
| 245 ## - TITLE STATEMENT | |
| Title | FLOOD FORECASTING USING HYBRID WAVELET NEURAL NETWORK MODEL |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.39(2), April |
| 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 | 3-11p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | The dynamic and accurate flood forecasting of daily stream flow processes of a river are important in the management of extreme events such as flash floods, floods and optimal design of water storage structures and drainage network. This paper aims to recommend a best hydrologic models are linear stochastic models autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA) and nonlinear models like Artificial neural network (ANN) and Wavelet neural network (WNN) for flood forecasting of Vamsadhara river in avelet neural network (WNN) is an hybrid modelling approach for forecasting of river flow using daily time series s data of river flow into sub series with low (approximation) and high (details) frequency, and these sub series were then used as input data for the artificial neural network (ANN). WNN flow data was collected from India-WRIS . 60% data was used for model calibration and 40% for validation. The one day ahead forecasting mpared. The comparison of model forecasting performance was conducted based upon different statistical indices and graphical criteria. The result indicates that WNN model is better than ANN, ARIMA and ARMA. |
| 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) | 12640 |
| Co-Author | Jeyakanthan, V. S. |
| 773 0# - HOST ITEM ENTRY | |
| Title | Journal of indian water resource society |
| Place, publisher, and date of publication | Roorkee Indian Institute of Technology Roorkee |
| International Standard Serial Number | 0970-6984 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | http://iwrs.org.in/journal/apr2019/4apr.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 |
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| Dewey Decimal Classification | School of Engineering & Technology (PG) | School of Engineering & Technology (PG) | Archieval Section | 20/11/2020 | 2020-2021046 | 20/11/2020 | 20/11/2020 | Articles Abstract Database |