• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1739-1756
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• 2021

Atmospheric nitrogen dioxide (NO₂) is mainly caused by anthropogenic emissions. It contributes to the formation of secondary pollutants and ozone through chemical reactions, and adversely affects human health. Although ground stations to monitor NO₂ concentrations in real time are operated in Korea, they have a limitation that it is difficult to analyze the spatial distribution of NO₂ concentrations, especially over the areas with no stations. Therefore, this study conducted a comparative experiment of spatial interpolation of NO₂ concentrations based on two linear-regression methods(i.e., multi linear regression (MLR), and regression kriging (RK)), and two machine learning approaches (i.e., random forest (RF), and support vector regression (SVR)) for the year of 2020. Four approaches were compared using leave-one-out-cross validation (LOOCV). The daily LOOCV results showed that MLR, RK, and SVR produced the average daily index of agreement (IOA) of 0.57, which was higher than that of RF (0.50). The average daily normalized root mean square error of RK was 0.9483%, which was slightly lower than those of the other models. MLR, RK and SVR showed similar seasonal distribution patterns, and the dynamic range of the resultant NO₂ concentrations from these three models was similar while that from RF was relatively small. The multivariate linear regression approaches are expected to be a promising method for spatial interpolation of ground-level NO₂ concentrations and other parameters in urban areas.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.38 no.2
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• pp.357-367
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• 2018

Recent earthquakes in Gyeongju and Pohang have raised interest in liquefaction in South Korea. Liquefaction, which is a phenomenon that excessive pore pressure is generated and the shear strength of soil is decreased by repeated loads such as earthquakes, causes severe problems such as ground subsidence and overturning of structures. Therefore, it is necessary to identify and prepare for the possibility of liquefaction in advance. In general, the possibility of liquefaction is quantitatively assessed using the Liquefaction Potential Index (LPI), but it takes a lot of time and effort for performing site response analysis which is essential for the liquefaction evaluation. In this study, a simple method to evaluate the liquefaction potential without executing the site response analysis in a downtown area with a lot of borehole data was proposed. In this simple method, the correlation between the average shear wave velocity of the target location ground and the LPI divided by thickness of liquefiable layer was established. And the applicable correlation equation for various rock outcrop accelerations were derived. Using the 104 boreholes information in Seoul, the correlation equation between LPI and the shear wave velocity (ground water level: 0m, 1m, 2m, 3m) is obtained and the possibility of liquefaction occurrence in Seoul and Gyeongju is evaluated. The applicability of the proposed simple method was verified by comparing the LPI values calculated from the correlation equation and the LPI values derived using the existing site response analysis. Finally, the distribution map of LPI calculated from the correlation was drawn using Kriging, a geostatistical technique.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.322-340
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• 2021

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.