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배덕원,김형준,윤광석,Bae,Deok-Won,Kim,Hyung-Jun,Yoon,Kwang-Seok 한국방재학회 2011 한국방재학회논문집 Vol.11 No.6
일반적으로 하천에서 취수나 하상유지를 위하여 설치되는 하천횡단구조물은 구조물 영향에 의해 국부적으로 유속이 증가하여 연결호안을 유실시킴으로서 제방붕괴에 따른 홍수피해가 가중될 수 있다. 또한, 현재 연결호안은 설계시 뚜렷한 설계기준이 없이 경험적인 판단에 의해 안전성을 판단함으로서 과소설계 또는 과대설계 될 우려가 있다. 본 연구에서는 하천횡단구조물 연결호안 설계기법의 개선점을 도출하기 위해 사석 및 블록호안에 대하여 수리실험을 수행하였다. 실험결과를 바탕으로 연결호안 설계시 1차원 접근유속을 대표유속으로 적용하는 방법의 특성을 파악하고 이탈조건에서의 국부유속을 고려한 호안재료 제원 및 연결호안 설치길이 결정 경험식을 제안하였다. The river-crossing structures are constructed to maintain river bed. Revetment is a structure which is installed around connection to protect weak points between levee and river-crossing structures from flood. It is, however, possible that revetment is collapsed by local velocity which is increased around structures. In addition, it is possible that design value is overestimated or underestimated since the stability of revetment is assessed based on empirical judgments without clear criteria. This study is focused on the improvement of techniques for revetments design around river-crossing structures through laboratory experiments on riprap and concrete block. The experimental results were used to figure out methods applying local velocity in the condition of collapse. As a result, the empirical formula, such as the sizes and the stability length of revetment, reflecting local velocity on the slope was proposed.
K-Means Clustering 기법과 원격탐사 자료를 활용한 탄소기반 글로벌 해양 생태구역 분류
김영준,배덕원,임정호,정시훈,추민기,한대현,Young Jun Kim,Dukwon Bae,Jungho Im,Sihun Jung,Minki Choo,Daehyeon Han 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
An acceleration of climate change in recent years has led to increased attention towards 'blue carbon' which refers to the carbon captured by the ocean. However, our comprehension of marine ecosystems is still incomplete. This study classified and analyzed global marine eco-provinces using k-means clustering considering carbon cycling. We utilized five input variables during the past 20 years (2001-2020): Carbon-based Productivity Model (CbPM) Net Primary Production (NPP), particulate inorganic and organic carbon (PIC and POC), sea surface salinity (SSS), and sea surface temperature (SST). A total of nine eco-provinces were classified through an optimization process, and the spatial distribution and environmental characteristics of each province were analyzed. Among them, five provinces showed characteristics of open oceans, while four provinces reflected characteristics of coastal and high-latitude regions. Furthermore, a qualitative comparison was conducted with previous studies regarding marine ecological zones to provide a detailed analysis of the features of nine eco-provinces considering carbon cycling. Finally, we examined the changes in nine eco-provinces for four periods in the past (2001-2005, 2006-2010, 2011-2015, and 2016-2020). Rapid changes in coastal ecosystems were observed, and especially, significant decreases in the eco-provinces having higher productivity by large freshwater inflow were identified. Our findings can serve as valuable reference material for marine ecosystem classification and coastal management, with consideration of carbon cycling and ongoing climate changes. The findings can also be employed in the development of guidelines for the systematic management of vulnerable coastal regions to climate change.
Synthetic Aperture Radar 인공위성 영상과 지형 자료를 활용한 하천 수변피복 분류 기계학습 알고리즘 개발
이재세,배덕원,김영준,임정호 (사)지오에이아이데이터학회 2023 GEO DATA Vol.5 No.3
Riverine environments play a crucial role in maintaining the stability of river ecosystems as well as biodiversity. Furthermore, the appropriate management of small rivers has a significant impact not only on stable water supplies but also on water resource management. Wide monitoring of the riverside environment including land covers and their changes is an important issue in water resource management. This study aims to develop a high-resolution (10 m) model for classifying riverside land cover by integrating Sentinel-1 synthetic aperture radar (SAR) data and terrestrial characteristics using machine learning algorithms. We constructed a total of 3,284 landcover reference point datasets near the four major rivers of South Korea with five classes: water, barren, grass, forest, and built-up. The Random Forest and Light Gradient Boosting Machine classification models were developed using eight input variables derived from SAR signal and digital terrain data. The models showed an overall cross-validation accuracy exceeding 80% while maintaining consistent spatial distributions, except for the barren class. The false alarms on barren would be corrected through additional sampling processes and incorporating optical characteristics in further study. The high-resolution riverside land cover maps are expected to contribute to the establishment of a comprehensive management system for water resources such as riverside land cover change detection, river ecosystem monitoring, and flood hazard management. Furthermore, the utilization of the next generation medium satellite 5 (C-band SAR) would improve the performance of riverside land cover classification algorithm in the future.