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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.
Current and Future Status of GIS-based Landslide Susceptibility Mapping: A Literature Review
Saro Lee 대한원격탐사학회 2019 大韓遠隔探査學會誌 Vol.35 No.1
Landslides are one of the most damaging geological hazards worldwide, threating both humans and property. Hence, there have been many efforts to prevent landslides and mitigate the damage that they cause. Among such efforts, there have been many studies on mapping landslide susceptibility. Geographic information system (GIS)-based techniques have been developed and applied widely, and are now the main tools used to map landslide susceptibility. We reviewed the status of landslide susceptibility mapping using GIS by number of papers, year, study area, number of landslides, cause, and models applied, based on 776 articles over the last 20 years (1999– 2018). The number of studies published annually increased rapidly over time. The total study area spanned 65 countries, and 47.7% of study areas were in China, India, South Korea, and Iran, where more than 500 landslides, 27.3% of all landslides, have occurred. Slope (97.6% of total articles) and geology (82.7% of total articles) were most often implicated as causes, and logistic regression (26.9% of total articles) and frequency ratio (24.7% of total article) models were the most widely used models. We analyzed trends in the causes of and models used to simulate landslides. The main causes were similar each year, but machine learning models have increased in popularity over time. In the future, more study areas should be investigated to improve the generalizability and accuracy of the results. Furthermore, more causes, especially those related to topography and soil, should be considered and more machine learning models should be applied. Finally, landslide hazard and risk maps should be studied in addition to landslide susceptibility maps.
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>
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.