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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/6235
Title: Artificial neural network model as a potential alternative for groundwater salinity forecasting
Authors: Banerjee, Pallavi
Singh V.S.
Chatttopadhyay K.
Chandra P.C.
Singh B.
Published in: Journal of Hydrology
Abstract: The paper evaluates the prospect of artificial neural network (ANN) simulation over mathematical modeling in estimating safe pumping rate to maintain groundwater salinity in island aquifers. Feed-forward ANN model with quick propagation (QP) as training algorithm has been used to forecast the salinity under varied pumping rates. The accuracy, generalization ability and reliability of the model are verified by real-time field data. The model is trained with 2 years of real-time field data and prediction on water quality with varying pumping rate is made for a span of 5 years. The same is then compared with both real-time field data and the prediction based on SUTRA (Saturated-Unsaturated Transport) computations. The proposed ANN model has surfaced as a simpler and more accurate alternative to the numerical method techniques. The ANN methodology using minimal lag and number of hidden nodes, along with the optimal number of spatial and temporal variables consistently produced the best performing network based simulation models. The prediction accuracy of the ANN model has been extended for another 5 years to further define the limit of pumping at a permissible level of groundwater salinity. © 2010 Elsevier B.V.
Citation: Journal of Hydrology (2011), 398(43894): 212-220
URI: https://doi.org/10.1016/j.jhydrol.2010.12.016
http://repository.iitr.ac.in/handle/123456789/6235
Issue Date: 2011
Keywords: Artificial neural network
Feed-forward neural network
Finite-element simulation model
Groundwater salinity
Pumping rate
Quick propagation algorithm
ISSN: 221694
Author Scopus IDs: 24480592700
35510471700
36774181100
57197230652
7405637879
Author Affiliations: Banerjee, P., National Geophysical Research Institute (CSIR), Hyderabad, AP, India
Singh, V.S., National Geophysical Research Institute (CSIR), Hyderabad, AP, India
Chatttopadhyay, K., Satyam Computer Services Limited, Hyderabad, AP, India
Chandra, P.C., Central Ground Water Board, Ministry of Water Resources Government of India, Patna, Bihar, India
Singh, B., Department of Science and Technology, Technology Bhavan, New Delhi, India
Corresponding Author: Banerjee, P.; Groundwater Building, National Geophysical Research Institute (CSIR), Hyderabad, AP 500 007, India; email: vns_pal@yahoo.co.in
Appears in Collections:Journal Publications [ES]

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