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Hydrogeology Journal

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01.12.2012 | Report

Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model

verfasst von: Saro Lee, Kyo-Young Song, Yongsung Kim, Inhye Park

Erschienen in: Hydrogeology Journal | Ausgabe 8/2012

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Abstract

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor’s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09 %. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.

Vorheriger Artikel Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm

Nächster Artikel Hydraulic testing using a versatile straddle packer system for improved transmissivity estimation in fractured-rock boreholes

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Titel: Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model
verfasst von: Saro Lee
Kyo-Young Song
Yongsung Kim
Inhye Park
Publikationsdatum: 01.12.2012
Verlag: Springer-Verlag
Erschienen in: Hydrogeology Journal / Ausgabe 8/2012
Print ISSN: 1431-2174
Elektronische ISSN: 1435-0157
DOI: https://doi.org/10.1007/s10040-012-0894-7

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