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박현배 ( Hyun-bae Park ),문성우 ( Seong-woo Moon ),주세정 ( Sejeong Ju ),이정음 ( Jeungeum Lee ),서용석 ( Yong-seok Seo ) 대한지질공학회 2024 지질공학 Vol.34 No.1
우리나라는 1980년까지 광물 지하자원 개발을 활발히 수행하였으나 이후 생산원가 대비 사업성이 크게 줄어들면서 많은 수의 폐광산이 발생되었다. 그 중 대부분은 광산개발 중단 후 오랜 시간이 지나 잔류성 침하가 발생할 가능성이 높은 상태에 있다. 하지만 그 발생 시기와 위치를 정확히 예측하기는 어려움이 있다. 따라서, 본 연구에서는 실제 지반침하가 발생한 사례를 분석하여 연속체 수치해석을 통해 지반침하의 발생 위치나 규모에 대한 예측 가능성을 살펴보고자 한다. 연구지역은 양산단층과 모량단층 사이에 위치하여 이들에 규제받는 ○○광산 지역이며, 이 지역에서는 2005~2009년에 걸쳐 3회의 지반침하가 발생된 바 있다. 지반침하지를 대상으로 시추조사 및 전기비저항 탐사를 수행하여 지층의 분포를 파악하였고, 실내시험을 통해 수치해석 시 적용되는 지반의 물리 · 역학적 특성을 파악하였다. 수치해석 결과 소성영역이 실제 지반침하 범위를 포함하는 형태로 나타나 연속체 해석으로도 채굴적에 의한 침하지 발생 위치 및 규모를 예측하는데 활용할 수 있을 것으로 판단된다. South Korea’s mining industry was actively developed until 1980, but subsequent declining profitability forced many mines to close. Most of the abandoned mines are susceptible to persistent subsidence because of the length of time since mining ceased. Accurate prediction of the locations and times of subsidence is difficult; therefore, this study aims to apply continuum analysis to past cases of subsidence to establish a method of predicting the location and magnitude of future subsidence. The study area is an area of ○○ mining located between the Yangsan fault zone and the Moryang fault zone, in which three subsidence events occurred between 2005 and 2009. Drilling surveys and electrical resistivity surveys were performed at subsidence sites determined the distribution of strata, and through laboratory tests obtained the physico-mechanical properties of the rock. Numerical analysis of the results found that the plastic status area includes the areas of actual subsidence and that continuum analysis can also be used to predict the location and magnitude of subsidence caused by mine goaf.
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기계학습을 이용한 광학 위성 영상 기반의 도시 내 수목 피복률 추정
배세정,손보경,성태준,이연수,임정호,강유진,Sejeong Bae,Bokyung Son,Taejun Sung,Yeonsu Lee,Jungho Im,Yoojin Kang 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R<sup>2</sup> of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R<sup>2</sup> of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.
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