Estimation of spatial variability of aquifer parameters from geophysical methods: a case study of Sindhudurg district, Maharashtra, India

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dc.contributor.author Das, Anasuya
dc.contributor.author Maiti, Saumen
dc.contributor.author Naidu, Suneetha
dc.contributor.author Gupta, Gautam
dc.date.accessioned 2017-11-07T09:04:17Z
dc.date.accessioned 2021-02-12T10:47:47Z
dc.date.available 2017-11-07T09:04:17Z
dc.date.available 2021-02-12T10:47:47Z
dc.date.issued 2017
dc.identifier.citation Stochastic Environmental Research and Risk Assessment, 31, 7, 1709–1726, doi: 10.1007/s00477-016-1317-4 en_US
dc.identifier.uri http://localhost:8080/xmlui/handle/123456789/1073
dc.description.abstract Accurate estimation of aquifer parameters, especially from crystalline hard rock area, assumes a special significance for management of groundwater resources. The aquifer parameters are usually estimated through pumping tests carried out on water wells. While it may be costly and time consuming for carrying out pumping tests at a number of sites, the application of geophysical methods in combination with hydro-geochemical information proves to be potential and cost effective to estimate aquifer parameters. Here a method to estimate aquifer parameters such as hydraulic conductivity, formation factor, porosity and transmissivity is presented by utilizing electrical conductivity values analysed via hydro-geochemical analysis of existing wells and the respective vertical electrical sounding (VES) points of Sindhudurg district, western Maharashtra, India. Further, prior to interpolating the distribution of aquifer parameters of the study area, variogram modelling was carried out using data driven techniques of kriging, automatic relevance determination based Bayesian neural networks (ARD-BNN) and adaptive neuro-fuzzy neural networks (ANFIS). In total, four variogram model fitting techniques such as spherical, exponential, ARDBNN and ANFIS were compared. According to the obtained results, the spherical variogram model in interpolating transmissivity, ARD-BNN variogram model in interpolating porosity, exponential variogram model in interpolating aquifer thickness and ANFIS variogram model in interpolating hydraulic conductivity outperformed rest of the variogram models. Accordingly, the accurate aquifer parameters maps of the study area were produced by using the best variogram model. The present results suggest that there are relatively high value of hydraulic conductivity, porosity and transmissivity at Parule, Mogarne, Kudal, and Zarap, which would be useful to characterize the aquifer system over western Maharashtra. en_US
dc.language.iso en en_US
dc.subject Hydraulic conductivity en_US
dc.subject Porosity en_US
dc.subject Transmissivity en_US
dc.subject Variogram en_US
dc.subject Bayesian neural networks en_US
dc.subject ANFIS en_US
dc.title Estimation of spatial variability of aquifer parameters from geophysical methods: a case study of Sindhudurg district, Maharashtra, India en_US
dc.type Article en_US
dc.identifier.accession 091615


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