Application of wann model for groundwater level forecasting in ur river watershed in tikamgarh district, India (Record no. 19137)

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003 - CONTROL NUMBER IDENTIFIER
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005 - DATE AND TIME OF LATEST TRANSACTION
control field 20230405122532.0
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) 20411
Author Ankur Kumar
245 ## - TITLE STATEMENT
Title Application of wann model for groundwater level forecasting in ur river watershed in tikamgarh district, India
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 28-40p.
520 ## - SUMMARY, ETC.
Summary, etc. The use of Aquifers as a source of water supply is increasing on a global scale, leading to over-exploitation of available groundwater blocks.<br/>Thus, there is an increasing demand for checking the groundwater levels for better and sustainable management of groundwater resources.<br/>To acquire knowledge about the factors affecting the entire groundwater system, one should know the important variables and how they vary<br/>over time. It is well known that the groundwater head is considered to be one of the most essential hydrological variables and hence, it is<br/>monitored and predicted frequently at different locations and at frequent time intervals. Particularly, the groundwater prediction in hard<br/>rock areas is a complex task with the use of physically-based models as compared to the data-driven models. Therefore, in this study, an<br/>attempt has been made to verify the adequacy as well as the efficacy of the Artificial Neural Network model (ANN) and Wavelet-ANN<br/>conjunction (WANN) models in the prediction of groundwater levels in the Ur River watershed in Tikamgarh district of Madhya Pradesh,<br/>India. Although the Ur river basin having mainly granite type of aquifer, the obtained results reveal that the WANN and ANN models can be<br/>used to predict the groundwater levels in this watershed. The application of the ANN model in the groundwater prediction gives a higher<br/>estimate of the RMSE values during calibration and validation as compared to those obtained with the application of the WANN model for<br/>each one of the observation wells. Further, the WANN model is capable to provide groundwater level prediction with higher efficiency as<br/>reflected by higher R2 values during calibration and validation as compared to the ANN model which indicates a substantial improvement in<br/>the model performance. Therefore, it can be concluded that the WANN model provides a significantly accurate prediction of groundwater<br/>levels as compared to the results of the ANN model. Besides, the comparison of the scatter plots of time series during calibration and<br/>validation indicates that the values of water level depth estimated by the WANN model are more precise than those estimated by the ANN.<br/>Thus, this paper reveals the significant features of ANN models for forecasting groundwater levels in hard rock aquifer and their<br/>performance enhancement with wavelet theory.
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) 20412
Co-Author Kale, Ravindra Vitthal
773 0# - HOST ITEM ENTRY
Title Journal of indian water resource society
International Standard Serial Number 0970-6984
Place, publisher, and date of publication Roorkee Indian Institute of Technology Roorkee
856 ## - ELECTRONIC LOCATION AND ACCESS
URL https://iwrs.org.in/journal/jan2021/5jan.pdf
Link text Click here
942 ## - ADDED ENTRY ELEMENTS (KOHA)
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    Dewey Decimal Classification     School of Engineering & Technology (PG) School of Engineering & Technology (PG) Archieval Section 05/04/2023   2023-0621 05/04/2023 05/04/2023 Articles Abstract Database
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