Environment and Water Resource Management / 837: Health Informatics / 838: Modelling and Simulation / 839: Power and Energy Sys 2016
DOI: 10.2316/p.2016.836-010
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Specific Groundwater Vulnerability Mapping: Case Study of Acid Mine Drainage in the Witbank Coalfield, South Africa

Abstract: This study highlights the usage of artificial neural networks in assessment of groundwater vulnerability. The network uses the DRIST input parameters (Depth to water level, Recharge, Impact of the vadose zone, Soils and Topography) as inputs and hydrochemistry data (Sulphate and Total Dissolved Solids (TDS)) as training data. The results of training and classification using sulphate and TDS were combined using a fuzzy (AND) operator to generate the groundwater vulnerability model. This technique was applied to… Show more

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Cited by 2 publications
(3 citation statements)
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References 15 publications
(21 reference statements)
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“…The scores for the A factor are 1, 3, 5, 7, and 10, for a storativity (m 3 /day/m) smaller than 2, between 2 and 20, in the interval [20,200), from 200 to 1000, and greater than 1000, respectively [66].…”
Section: Dravmentioning
confidence: 99%
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“…The scores for the A factor are 1, 3, 5, 7, and 10, for a storativity (m 3 /day/m) smaller than 2, between 2 and 20, in the interval [20,200), from 200 to 1000, and greater than 1000, respectively [66].…”
Section: Dravmentioning
confidence: 99%
“…Sakala et al [66] used the same model and a neural network approach to generate a groundwater vulnerability model. The network used as input the DRIST parameters, and as the training dataset, the sulfate and Total Dissolved Solids (TDS) concentrations retrieved from five groundwater samples.…”
Section: Drist and Modified Drasticmentioning
confidence: 99%
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