• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.907-915
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• 2018

With the increase of metal usage in various industries, metal pollution and ecological toxicity in the environmental system have become a significant concern. A geo-statistical index has been widely used to determine contamination level with normalization through a background value. In this study, geo-statistical indexes such as an enrichment factor, accumulation index, and potential ecological risk index were used to assess metal pollution in soil at locations associated with shipbuilding manufacturing industries. Metal contamination, especially of Cu and Pb, was observed in some samples located closer to manufacturing sites. Enrichment factor and accumulation (IGEO) values were indicative of concerning levels of soil contamination in specific samples, and the soil contamination could be induced by anthropogenic sources. In further study, after more detailed sampling for soil and potential pollution sources, high interpretation techniques such as Pb isotope analysis and X-ray analysis will be needed to investigate source identification.

• The Journal of Engineering Geology
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• v.27 no.1
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• pp.67-79
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• 2017

Evaluation of the weathering index using the quantitative element composition of rocks is very effective in predicting the degree of weathering of rocks and the secondary weathering residuals. While the process of weathering varies according to the types of rocks, the study of weathering in Korea is concentrated on acidic igneous rocks. This study calculated the weathering indices using whole rock analysis (X-ray fluorescence analysis) of sandstone, mudstone, and shale belonging to clastic sedimentary rocks. The statistical significance of the indices was examined based on the correlation of the calculated weathering indices. Clastic sedimentary rocks showed higher significance of Wp, CIA, CIW and PIA weathering index indicating weathering of feldspar. Chemical Index of alteration (CIA) has the advantage of predicting weathering pathway and clay mineral production, but it is effective to consider chemical index of weathering index (CIW) simultaneously to improve accuracy. In order to reduce uncertainties due to carbonate rocks and to estimate the accurate weathering index, rock samples with high CaO content should be excluded from the evaluation of weathering index.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.61.1-61.1
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• 2013

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.1-11
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• 2021

Detection of surface water features including river, wetland, reservoir from the satellite imagery can be utilized for sustainable management and survey of water resources. This research compared the water indices derived from the multispectral bands and the machine learning technique for detecting the surface water features from he Landsat-8 satellite image acquired in Daegu through the following steps. First, the NDWI(Normalized Difference Water Index) image and the MNDWI(Modified Normalized Difference Water Index) image were separately generated using the multispectral bands of the given Landsat-8 satellite image, and the two binary images were generated from these NDWI and MNDWI images, respectively. Then SVM(Support Vector Machine), the widely used machine learning techniques, were employed to generate the land cover image and the binary image was also generated from the generated land cover image. Finally the error matrices were used for measuring the accuracy of the three binary images for detecting the surface water features. The statistical results showed that the binary image generated from the MNDWI image(84%) had the relatively low accuracy than the binary image generated from the NDWI image(94%) and generated by SVM(96%). And some misclassification errors occurred in all three binary images where the land features were misclassified as the surface water features because of the shadow effects.

• Journal of The Korean Astronomical Society
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• v.45 no.2
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.70-82
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• 2020

Recently, a lot of damages have been caused by urban flooding, and heavy rainfall that temporarily occur are the main causes of these phenomenons. The damages caused by urban flooding are identified as the change in the water balance in urban areas. To indirectly identify it, this research analyzed the change in the remote sensing indices on each land cover before and after heavy rainfall by utilizing daily precipitation data and multi-temporal Sentinel-2 satellite imagery. Cases of heavy rain advisory and warning were selected based on the daily precipitation data. And statistical fluctuation were compared by acquiring Sentinel-2 satellite images during the corresponding period and producing them as NDVI, NDWI and NDMI images about each land cover with a radius of 1,000 m based on the Seoul Weather Station. As a result of analyzing the maximum value, minimum value, mean and fluctuation of the pixels that were calculated in each remote sensing index image, there was no significant changes in the remote sensing indices in urban areas before and after heavy rainfall.

• Journal of the Korean GEO-environmental Society
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• v.16 no.1
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• pp.17-27
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• 2015

Recently, Korean government has tried out to set up earthquake hazards prevention system. In the system, several geotechnical hazard maps including liquefaction hazard map and landslide hazard map for the whole country have drawn to consider the domestic seismic characteristics. To draw the macro liquefaction hazard map, big data of site investigations in metropolitan areas and provincial areas has to be verified for its application. In this research, we carried out site response analyses using 522 borehole site investigation data in S city during a desirable earthquake. The soil classification was separately compared to shear wave velocity considering the uncertainty of site investigation data. Probability distribution and statistical analysis for the results of site response analyses was applied to the feasibility study. Finally, we suggest a new site amplification coefficient, hereby presented with the similar results of liquefaction hazard mapping using the calculated liquefaction potential index by the site response analyses. Above-mentioned study will be expected to help to follow research and draw liquefaction hazard map in moderate seismic region.

• Journal of the Korean GEO-environmental Society
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• v.15 no.6
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• pp.67-73
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• 2014

Landslide disasters including debris flows are the one of the most frequent natural disasters in Korea, and losses of lives and property damages due to these catastrophic events have been increased every year. Various mitigation programs and related policies have been conducted in order to respond and prepare landslide disasters. Most landslide reduction programs are, however, focused on recovery actions after the disasters and lead to unrealistic consequences to the affected people and their properties. The main objective of this study, therefore, is to evaluate the landslide hazard based on the identification of geomorphological features, which is for the preparedness of the landslide disasters. Two methodologies, SINMAP and vector dispersion analyses are used to simulate those characteristics where landslides are actually located. Results showed that both methods well discriminate geomorphic features between stable and unstable domains. This proves that geomorphological characteristics well describe a relationship with the existing landslide hazard. SINMAP analysis which is based on the consecutive model considering external factors like infiltration is well identify the landslide hazard especially for debris flow type landslides rather than vector dispersion focusing on a specific area. Combining with other methods focusing specific characteristics of geomorphological feature, accurate landslide hazard assessments are implemented.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1301-1314
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• 2022

Normalized Difference Vegetation Index (NDVI) is utilized as an indicator to represent the vegetation condition on the land surface in various applications such as land cover, crop yield, agricultural drought, soil moisture, and forest disaster. However, satellite optical sensors for visible and infrared rays cannot see through the clouds, so the NDVI of the cloud pixel is not a valid value for the land surface. This study proposed a real-time correction of the underestimation noise for GEO-KOMPSAT-2A (GK2A) daily NDVI and made sure its feasibility through the quantitative comparisons with Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI and the qualitative interpretation of time-series changes. The underestimation noise was effectively corrected by the procedures such as the time-series correction considering vegetation phenology, the outlier removal using long-term climatology, and the gap filling using rigorous statistical methods. The correlation with MODIS NDVI was higher, and the difference was lower, showing a 32.7% improvement compared to the original NDVI product. The proposed method has an extensibility for use in other satellite products with some modification.