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首页> 外文期刊>Journal of Hydroinformatics >Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors
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Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors

机译：利用频率比和证据置信函数及人工神经网络模型绘制地下水生产率潜力图：关注地形因素

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This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.
机译：这项研究使用韩国Okcheon市的地理信息系统（GIS）中的三种不同模型分析了地下水生产率潜力（GPP）。具体来说，我们在这项研究中使用了各种地形因素。该模型基于地下水生产率（比容（SPC）和透射率（T））与水文地质因素之间的关系。首先收集，处理和处理地形，地质，地貌，土地利用和土壤数据，并将其输入空间数据库。从86口井收集了T和SPC数据。使用未用于模型训练的井数据在曲线分析区域下验证了生成的GPP图。使用人工神经网络（ANN），频率比（FR）和证据信念函数（EBF）模型的GPP的T准确度分别为82.19％，81.15％和80.40％。同样，用于SPC的ANN，FR和EBF模型的准确度分别为81.67％，81.36％和79.89％。结果表明，人工神经网络模型可用于开发地下水资源。

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