• Geomechanics and Engineering
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• v.36 no.5
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• pp.441-453
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• 2024

Sinkhole subsidence and collapse is a common geohazard often formed in karst areas such as the state of Florida, United States of America. To predict the sinkhole occurrence, we need to understand the formation mechanism of sinkhole and its karst hydrogeology. For this purpose, investigating the factors affecting sinkholes is an essential and important step. The main objectives of the presenting study are (1) the development of a machine learning (ML)-based model, namely C5.0 decision tree (C5.0 DT), for the prediction of sinkhole susceptibility, which accounts for sinkhole/subsidence inventory and sinkhole contributing factors (e.g., geological/hydrogeological) and (2) the construction of a regional-scale sinkhole susceptibility map. The study area is east central Florida (ECF) where a cover-collapse type is commonly reported. The C5.0 DT algorithm was used to account for twelve (12) identified hydrogeological factors. In this study, a total of 1,113 sinkholes in ECF were identified and the dataset was then randomly divided into 70% and 30% subsets for training and testing, respectively. The performance of the sinkhole susceptibility model was evaluated using a receiver operating characteristic (ROC) curve, particularly the area under the curve (AUC). The C5.0 model showed a high prediction accuracy of 83.52%. It is concluded that a decision tree is a promising tool and classifier for spatial prediction of karst sinkholes and subsidence in the ECF area.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.2
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• pp.86-102
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• 2012

For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, For predictive flooded area susceptibility mapping, this study applied and verified probability model and the frequency ratio using a geographic information system (GIS) and frequency raio. Flooded areas were identified in the study area of field surveys, and maps of the topography, geology, landcover and green infrastructure were constructed for a spatial database. The factors that influence flooded areas occurrence, such as slope gradient, slope, aspect and curvature of topography and distance from darinage, were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. The frequency ratio coefficient is overlaid for flooded areas susceptibility mapping as each factor's ratings. Then the flooded areas susceptibility map was verified and compared using the existing flooded areas. As the verification results, the frequency ratio model showed 82% in prediction accuracy. The method can be used to reduce hazards associated with flooded areas and to plan land use.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.107-113
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• 2015

Purpose: Susceptibility weighted imaging (SWI) is a new magnetic resonance technique that can exploit the magnetic susceptibility differences of various tissues. Intracranial hemorrhage (ICH) looks a dark blooming on the magnitude images of SWI. However, the pattern of ICH on phase images is not well known. The purpose of this study is to characterize hemorrhagic lesions on the phase images of SWI. Materials and Methods: We retrospectively enrolled patients with ICH, who underwent both SWI and precontrast CT, between 2012 and 2013 (n = 95). An SWI was taken, using the 3-tesla system. A phase map was generated after postprocessing. Cases with an intracranial hemorrhage were reviewed by an experienced neuroradiologist and a trainee radiologist, with 10 years and 3 years of experience, respectively. The types and stages of the hemorrhages were determined in correlation with the precontrast CT, the T1- and T2-weighted images, and the FLAIR images. The size of the hemorrhage was measured by a one- directional axis on a magnitude image of SWI. The phase values of the ICH were qualitatively evaluated: hypo-, iso-, and hyper-intensity. We summarized the imaging features of the intracranial hemorrhage on the phase map of the SWI. Results: Four types of hemorrhage are observed: subdural and epidural; subarachnoid; parenchymal hemorrhage; and microbleed. The stages of the ICH were classified into 4 groups: acute (n = 34); early subacute (n = 11); late subacute (n = 15); chronic (n = 8); stage-unknown microbleeds (n = 27). The acute and early subacute hemorrhage showed heterogeneous mixed hyper-, iso-, and hypo-signal intensity; the late subacute hemorrhage showed homogeneous hyper-intensity, and the chronic hemorrhage showed a shrunken iso-signal intensity with the hyper-signal rim. All acute subarachnoid hemorrhages showed a homogeneous hyper-signal intensity. All parenchymal hemorrhages (> 3 mm) showed a dipole artifact on the phase images; however, microbleeds of less than 3 mm showed no dipole artifact. Larger hematomas showed a heterogeneous mixture of hyper-, iso-, and hypo-signal intensities. Conclusion: The pattern of the phase value of the SWI showed difference, according to the type, stage, and size.

• Journal of the Korean Association of Geographic Information Studies
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• v.8 no.1
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• pp.75-87
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• 2005

This study aims at predicting and mapping of the landslide susceptibility in the Geumho river watershed using GIS and Remote Sensing techniques. We constructed the spatial database of affecting factors such as slope angle, slope aspect, lithology, landuse, and vegetation index (NDVI) at a $30m{\times}30m$ resolution. The landslide susceptibility of the study area was predicted through overlay analysis and adding up estimation matrix, and the predicted map of landslide susceptibility with six categories (stable, very low, low, moderate, high, very high) was constructed. As the results, it showed that the very high susceptibility zones made up approximately 0.3% of the total study area, and these zones were mainly distributed in the forest area with the high slope angle and low vegetation index.

• Economic and Environmental Geology
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• v.37 no.5
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• pp.521-532
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• 2004

The aim of this study is to evaluate the susceptibility of landslides at Kangneung area, Korea, using a Geographic Information System (GIS) and remote sensing. Landslide locations were identified from interpretation of satellite image and field surveys. The topographic, soil, forest, geologic, lineament and land cover data were collected, processed and constructed into a spatial database using GIS and remote sensing data. Using frequency ratio model which is one of the probability model, the relationships between landslides and related factors such as slope, aspect, curvature and type of topography, texture, material, drainage and effective thickness of soil, type, age, diameter and density of wood, lithology, distance from lineament and land cover were calculated as frequency ratios. Then, the frequency ratio were summed to calculate a landslide susceptibility indexes and the landslide susceptibility maps were generated using the indexes. The results of the analysis were verified and cross-validated using actual landslide location data. The verification results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.179-193
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• 2019

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.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2002.04a
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• pp.101-109
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• 2002

A lot of SSRs (simple sequence repeats) in peach and pear from enriched genomic libraries and in peach from a cDNA library were developed. These SSRs were applied to other related species, giving phenograms of 52 Prunus and 60 pear accessions. Apple SSRs could also be successfully used in Pyrus spp. Thirteen morphological traits were characterized on the basis of the linkage map obtained from an $F_2$ population of peach. This map was compiled with those morphological markers and 83 DNA markers, including SSR markers used as anchor loci, to compare different peach maps. Molecular markers tightly linked to new root-knot nematode resistance genes were also found. A linkage map including disease-related genes, pear scab resistance and black spot susceptibility, in the Japanese pear Kinchaku were constructed using 118 RAPD markers. Another linkage map, of the European pear Bartlett, was also constructed with 226 markers, including 49 SSRs from pear, apple, peach and chewy. Maps of other Japanese pear cultivars, i.e., Kousui and Housui, were also constructed. These maps were the first results of pear species.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2002.04b
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• pp.101-109
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• 2002

A lot of SSRs (simple sequence repeats) in peach and pear from enriched genomic libraries and in peach from a cDHA library were developed. These SSRs were applied to other related species, giving phenograms of 52 Prunus and 60 pear accessions. Apple SSRs could also be successfully used in Pyrus spp. Thirteen morphological traits were characterized on the basis of the linkage map obtained from an Fa population of peach. This map was compiled with those morphological markers and 83 DHA markers, including SSR markers used as anchor loci, to compare different peach maps. Molecular markers tightly linked to new root-knot nematode resistance genes were also found. A linkage map including disease-related genes, pear scab resistance and black spot susceptibility, in the Japanese pear Kinchaku were constructed using 118 RAPD markers. Another linkage map, of the European pear Bartlett, was also constructed with 226 markers, including 49 SSRs from pear, apple, peach and cherry. Maps of other Japanese pear cultivars, i.e., Kousui and Housui, were also constructed. These maps were the first results of pear species.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.6.1-6.8
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• 2022

Background: With limited information available, the association among urinary tract infections, urease-producing bacteria and the presence of magnesium ammonium phosphate (MAP) urolithiasis in canines in Thailand requires more study. Objectives: This study aimed to investigate the association between demographic characteristics of canines and the presence of MAP urolithiasis in canines, and to evaluate antimicrobial susceptibility patterns of bacteria isolated from canine uroliths. Methods: A total of 56 canines admitted for treatment with surgical removal of uroliths were recruited. Demographic characteristics and clinical chemistry data were recorded. Bacteria isolated from the removed uroliths were identified. Chemical compositions of the uroliths were analyzed by Fourier transform infrared spectrometer. Potential risk factors were determined with univariable and multivariable logistic regression analyses. Results: Of 56 canine urolithiasis, bacteria were isolated from uroliths of 38 canines (27 MAP and 11 non-MAP) but not from uroliths of 18 canines (5 MAP and 13 non-MAP). The most common bacteria found in nidus of MAP uroliths was Staphylococcus pseudintermedius (approximately 51%). An antimicrobial resistance was frequently found in Staphylococci isolates (42.86%). Multivariate logistic regression analysis showed that the predictors of MAP urolith in canine urolithiasis were being female (p = 0.044; adjusted odds ratio [OR], 10.22; 95% confidence interval [CI], 1.06-98.24) and the positive urolith culture (p = 0.012; adjusted OR, 8.60; 95% CI, 1.60-46.30). Conclusions: Our results indicate that S. pseudintermedius (a urease-producing bacterium) is the major causative bacteria of MAP uroliths. A positive urolith culture and being female are risk factors of MAP urolithiasis in canines.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.116-127
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• 2011

This study conducted landslide susceptibility analysis using logistic regression. The performance of prediction model needs to be evaluated considering two aspects such as a goodness of fit and a prediction accuracy. Thus to gain more objective prediction results in this study, the prediction performance of the applied model was evaluated considering two such evaluation aspects. The selected study area is located between Inje-eup and Buk-myeon in the middle of Kwangwon. Landslides in the study area were caused by heavy rain in 2006. Landslide causal factors were extracted from topographic map, forest map and soil map. The evaluation of prediction model was assessed based on the area under the curve of the cumulative gain chart. From the results of experiments, 87.9% in the goodness of fit and 84.8% in the cross validation were evaluated, showing good prediction accuracies and not big difference between the results of the two evaluation methods. The results can be interpreted in terms of the use of environmental factors which are highly related to landslide occurrences and the accuracy of the prediction model.