• The Journal of Engineering Geology
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• v.31 no.1
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• pp.1-17
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• 2021

It is important to evaluate the fracture network in a rock volume because fractures control the ground conditions and fluid flow characteristics. Therefore, various attempts have been made to quantify fracture networks to better understand ground and flow conditions. The use of fracture density alone (a quantitative parameter based on geometric analysis) does not fully explain the evolution of fracture networks, or quantify the spatial relationship (e.g. connectivity) of fractures in a rock mass. Therefore, the need for fracture network characterization based on topological analysis has recently emerged. In Korea however, the topological analysis of fracture networks within a rock mass has rarely been studied. As such, the definition of the topological analysis of fracture networks and the graph theory related to the topological analysis are briefly summarized in this study. We also introduce an application method for these analyses to fracture characterization. If the topological method is used for the analysis of fracture networks, it can also be adopted to analyze fluid flow characteristics of groundwater, characterize petroleum reservoirs, and analyze the evolution of a fracture network. In addition, topological analysis can be useful for site selection of major facilities such as nuclear waste disposal sites because it can be used to evaluate the stability of the potential sites.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1239-1248
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• 2021

In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model's performance through K-fold cross validation.

• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.4
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• pp.50-63
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• 2018

The rapid urbanization had led to a distortion of natural hydrological cycle system. The change in hydrological cycle structure is causing streamflow depletion, changing the existing use tendency of water resources. To manage such phenomena, a streamflow depletion impact assessment technology to forecast depletion is required. For performing such technology, it is indispensable to build GIS-based spatial data as fundamental data, but there is a shortage of related research. Therefore, this study was conducted to use the use of GIS-based time series spatial data for streamflow depletion assessment. For this study, GIS data over decades of changes on a national scale were constructed, targeting 6 streamflow depletion impact factors (weather, soil depth, forest density, road network, groundwater usage and landuse) and the data were used as the basic data for the operation of continuous hydrologic model. Focusing on these impact factors, the causes for streamflow depletion were analyzed depending on time series. Then, using distributed continuous hydrologic model based DrySAT, annual runoff of each streamflow depletion impact factor was measured and depletion assessment was conducted. As a result, the default value of annual runoff was measured at 977.9mm under the given weather condition without considering other factors. When considering the decrease in soil depth, the increase in forest density, road development, and groundwater usage, along with the change in land use and development, and annual runoff were measured at 1,003.5mm, 942.1mm, 961.9mm, 915.5mm, and 1003.7mm, respectively. The results showed that the major causes of the streaflow depletion were lowered soil depth to decrease the infiltration volume and surface runoff thereby decreasing streamflow; the increased forest density to decrease surface runoff; the increased road network to decrease the sub-surface flow; the increased groundwater use from undiscriminated development to decrease the baseflow; increased impervious areas to increase surface runoff. Also, each standard watershed depending on the grade of depletion was indicated, based on the definition of streamflow depletion and the range of grade. Considering the weather, the decrease in soil depth, the increase in forest density, road development, and groundwater usage, and the change in land use and development, the grade of depletion were 2.1, 2.2, 2.5, 2.3, 2.8, 2.2, respectively. Among the five streamflow depletion impact factors except rainfall condition, the change in groundwater usage showed the biggest influence on depletion, followed by the change in forest density, road construction, land use, and soil depth. In conclusion, it is anticipated that a national streamflow depletion assessment system to be develop in the future would provide customized depletion management and prevention plans based on the system assessment results regarding future data changes of the six streamflow depletion impact factors and the prospect of depletion progress.