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Lee, Sunmin,Kim, Jeong-Cheol,Jung, Hyung-Sup,Lee, Moung Jin,Lee, Saro Informa UK (TaylorFrancis) 2017 Geomatics, natural hazards and risk Vol.8 No.2
<P>Since flood frequency increases with the impact of climate change, the damage that is emphasized on flood-risk maps is based on actual flooded area data; therefore, flood-susceptibility maps for the Seoul metropolitan area, for which random-forest and boosted-tree models are used in a geographic information system (GIS) environment, are created for this study. For the flood-susceptibility mapping, flooded-area, topography, geology, soil and land-use datasets were collected and entered into spatial datasets. From the spatial datasets, 12 factors were calculated and extracted as the input data for the models. The flooded area of 2010 was used to train the model, and the flooded area of 2011 was used for the validation. The importance of the factors of the flood-susceptibility maps was calculated and lastly, the maps were validated. As a result, the distance from the river, geology and digital elevation model showed a high importance among the factors. The random-forest model showed validation accuracies of 78.78% and 79.18% for the regression and classification algorithms, respectively, and boosted-tree model showed validation accuracies of 77.55% and 77.26% for the regression and classification algorithms, respectively. The flood-susceptibility maps provide meaningful information for decision-makers regarding the identification of priority areas for flood-mitigation management.</P>
Saro Lee,최재원,우익 한국지질과학협의회 2004 Geosciences Journal Vol.8 No.1
The authors have evaluated the effect of spatial res-olution on the accuracy of landslide susceptibility mapping. Forthis purpose, landslide locations were identified from the interpre-tation of aerial photographs and field surveys in the Boun regionof Korea. Topography, soil, forest, geological, lineament and land-database using GIS and remote sensing data. The 15 factors thatinfluenced landslide occurrence were extracted and calculatedfrom the spatial database at 5, 10, 30, 100 and 200 m spatial res-olution. Hazardous landslide areas were analyzed and mappedusing the landslide-occurrence factors by employing a probabilitymodels frequency ratio for the five spatial resolutions. The resultsof the analysis were verified using the landslide location data andarea under success rate curve. The spatial resolutions of 5, 10 and30 m showed similar results (the normalized area values 0.97, 1.00and 0.92, respectively), but the 100 and 200 m spatial resolutionsshowed less well-verified data (the normalized area values 0.48,1:5,000-1:50,000, the 5, 10 and 30 m spatial resolutions had a sim-ilar accuracy, but the 100 and 200 m spatial resolutions had alower accuracy. From this, we conclude that spatial resolution hasan effect on the accuracy of landslide susceptibility, as it is depen-dent on the input map. At least, less than 30 m resolution is needfor landslide analysis in Korea where most of map scale is in therange 1:5,000-1:50,000.
Development and application of a GIS based groundwater modeling system
Lee, Saro,Park, Eungyu,Cho, Min-Joe Korea Spatial Information Society 2002 한국공간정보학회지 Vol.10 No.4
To carry out systematic groundwater assessment, exploration and management and to use these for protection of optimal groundwater yield, a data analysis and management system is required. Thus, the object of this research was to develop and apply software that integrates GIS and groundwater modeling: GISGAM (GIS for groundwater analysis and management system). The GIS program ArcView and the groundwater-modeling program MODFLOW were used for the GISGAM. The program components consist of a pre-processor, a processor, and a post-processor for groundwater modeling. In addition, GIS functions such as input, manipulation, analysis and output of data were embedded into the program. In applying the program to pilot area, topography, geology, soil, land use and well databases, and a groundwater flow model were constructed for the study area. This case study revealed the advantage and convenience of groundwater modeling using GIS capabilities. By integrating GIS and the groundwater model, the impact of changing values of hydrogeological constants on model results could be more easily evaluated.
Spatial prediction of ground subsidence susceptibility using an artificial neural network.
Lee, Saro,Park, Inhye,Choi, Jong-Kuk Springer Verlag 2012 Environmental management Vol.49 No.2
<P>Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, 'distance from fault' had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.</P>
Lee, Saro,Kim, Yong-Sung,Oh, Hyun-Joo Elsevier 2012 Journal of environmental management Vol.96 No.1
<P><B>Abstract</B></P><P>The aim of this study is to analyze the relationship among groundwater productivity data including specific capacity (SPC) and transmissivity (T) as well as its related hydrogeological factors in a bedrock aquifer, and subsequently, to produce the regional groundwater productivity potential (GPP) map for the area around Pohang City, Korea using a geographic information system (GIS) and a weights-of-evidence (WOE) model. All of the related factors, including topography, lineament, geology, forest, and soil data were collected and input into a spatial database. In addition, SPC and T data were collected from 83 and 81 well locations, respectively. Four dependent variables including SPC values of ≥6.25m<SUP>3</SUP>/d/m (Case 1) and T values of ≥3.79m<SUP>2</SUP>/d (Case 3) corresponding to a yield (Y) of ≥500m<SUP>3</SUP>/d, and SPC values of ≥3.75m<SUP>3</SUP>/d/m (Case 2) and T values of ≥2.61m<SUP>2</SUP>/d (Case 4) corresponding to a Y of ≥300m<SUP>3</SUP>/d were also input into a spatial database. The SPC and T data were randomly selected in an approximately 70:30 ratio to train and validate the WOE model. Tests of conditional independence were performed for the used factors. To assess the regional GPP for each dependent variable, W<SUP>+</SUP> and W<SUP>−</SUP> of each factor’s rating were overlaid spatially. The results of the analysis were validated using area under curve (AUC) analysis with the existing SPC and T data that were not used for the training of the model. The AUC of Cases 1, 2, 3 and 4 showed 0.7120, 0.6893, 0.6920, and 0.7098, respectively. In the case of the dependent variables, Case 1 had an accuracy of 71.20% (AUC: 0.7120), which is the best result produced in this analysis. Such information and the maps generated from it could be used for groundwater management, a practice related to groundwater resource exploration.</P> <P><B>Highlights</B></P><P>► We analyze the relationship among groundwater productivity data and its related factors. ► The productivity data include specific capacity (SPC) and transmissivity (T) in the study. ► The productivity potential map is produced using a GIS and weights-of-evidence model. ► The relationship and maps could be used for groundwater management.</P>