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재해영향평가 협의내용의 이행도 제고를 위한 사후관리제도 개선방안 연구
장광진(Jang, Kwangjin),곽창재(Kwak, Changjae) 한국방재학회 2020 한국방재학회논문집 Vol.20 No.1
본 연구는 최근 5년간 이행실태 점검 결과자료 분석을 통해 재해영향평가 제도 관련 연구에서 그간 논의되지 않았던 「재해영향평가등의 협의」제도의 협의내용 이행 등 사후관리와 관련한 제도의 개선방안을 제안하였다. 법·제도적 관점의 개선방안은1) 사업시행자 및 관리책임자를 대상으로 협의내용 관리 및 제도관련 교육, 2) 협의내용 관리 미이행 시 과태료 등 최근2018년 10월부로 강화된 법령을 엄격히 적용하고, 우수 사업장에 대해서는 인센티브를 부여하는 방안 명시, 3) 준공 이후재해 저감시설에 대한 사후 관리주체를 명확히 지정토록 법적 근거 마련이다. 운영·관리적 관점의 개선방안은 1) 협의기관에서운영하고 있는 조치계획 관련 서식 개선, 2) 시행규칙 별지 2호 서식에 본 연구에서 제안하는 협의내용 관리대장 서식 추가, 3) 사업시행자측에 협의내용 관리대장 작성 매뉴얼 제공으로 요약된다. 본 연구는 그간 논의가 부족했던 재해영향평가제도의사후관리 측면을 집중 고찰함으로써, 실효성 있는 제도개선방안을 제안하였다는 점에서 그 의의가 있다. This study proposes an improvement plan of follow-up management systems to the current Disaster Impact Assessment System (DIAS), which has been insufficiently addressed in previous research, which is made especially clearthrough an analysis of results of the implementation status check over the last five years. Legal and institutional recommendations are as follows: 1) Providing adequate training on the management method of consultation comments to a project owner/superintendent of consultation content; 2) Strictly enforcing laws and regulations that were strengthened as of October 2018, especially regarding fines, and offering incentives for outstanding workplaces and people in charge of projects; and 3) Establishing legal grounds to clearly designate post-management director personnel for disaster reduction facilities after completion of construction. Recommendations in terms of operation and management are as follows: 1) Improving an action plan form; 2) Adding a maintenance register form to Form 2 of the Enforcement Rules; and 3) Supplying a manual for consultation content management to project operators. This study is meaningful in that it suggests an effective improvement plan by focusing on the follow-up management aspect of DIAS, which have not previously been discussed.
격자 기반 침수위험지도 작성을 위한 기계학습 모델별 성능 비교 연구 - 2016 태풍 차바 사례를 중심으로 -
한지혜,곽창재,김구윤,이미란,Jihye Han,Changjae Kwak,Kuyoon Kim,Miran Lee 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.5
This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.