Comment on "Artificial neural network model as a potential alternative for groundwater salinity forecasting" by Pallavi Banerjee et al. [J. Hydrol. 398 (2011) 212–220]

Sreekanth, J., and Datta, Bithin (2012) Comment on "Artificial neural network model as a potential alternative for groundwater salinity forecasting" by Pallavi Banerjee et al. [J. Hydrol. 398 (2011) 212–220]. Journal of Hydrology, 420-421. pp. 419-420.

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Abstract

[Extract] Management of coastal aquifers for maintaining the water quality within permissible limits is an important groundwater management problem. Modeling saltwater intrusion is particularly challenging because of the density dependence of the saltwater intrusion process necessitating the simultaneous solution of flow and transport equations. Banerjee et al. has developed an artificial neural network (ANN) based model to optimize the aquifer exploitation to maintain the water quality within permissible limits. ANN model is developed for a real coastal aquifer in the Lakshadweep group of islands in India. The application of the methodology to the real life case study very well establishes the practical utility of the heuristic modeling tools like the ANN. The discussers would like to comment on the broader literature available on groundwater salinity predictions and management for coastal areas and about the ANN model development.

Item ID: 21192
Item Type: Article (Commentary)
Keywords: groundwater; coastal aquifer; ANN
ISSN: 0022-1694
Date Deposited: 15 May 2012 04:12
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
09 ENGINEERING > 0907 Environmental Engineering > 090799 Environmental Engineering not elsewhere classified @ 50%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%
Citation Count from Web of Science Web of Science 2
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