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

Lee, Saro; Song, Kyo-Young; Kim, Yongsung; Park, Inhye

Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model | Cartographie régionale du potentiel de productivité des aquifères à partir d’un système d’information géographique base sur un modèle de réseau de neurones artificiels Mapeo de la productividad potencial de agua subterránea regional usando un sistema de información geográfica (SIG) basado en un modelo de redes neuronales artificiales 基于人工神经网络模拟的GIS系统绘制区域地下水开采潜力图 인공신경망 모델에 기반한 지리정보시스템(GIS)을 이용한 광역적 지하수 부존 가능성도 작성 Mapeamento do potencial de produtividade regional de águas subterrâneas usando um modelo de rede neural artificial baseado num sistema de informação geográfica (SIG)

2012

Lee, Saro | Song, Kyo-Young | Kim, Yongsung | Park, Inhye

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.

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