Early warnings have been developed to provide rapid earthquake information, allowing people to prepare as much time as possible. However, since it takes several seconds for an earthquake warning to be issued, the blind zone is inevitable. To reduce the blind zone, information from a single observatory is used to operate an on-site earthquake warning. However, false and missed alarms are still high, requiring continued research and validation. This study predicted Peak Ground Acceleration (PGA) using the characteristic data to reduce false and missed alarms in on-site earthquake warnings. A machine learning prediction model was created using the initial P-wave parameters developed from the characteristic data to achieve this. Then, the model was used to predict the maximum ground acceleration in the southeastern region of the Korean Peninsula. The expected results for six target earthquakes were confirmed to have a standard deviation within 0.3 compared to the observed PGA and the values within ±2 sigma. This method is expected to help develop an on-site early warning system for earthquakes.

본 논문은 동남권역(부산, 울산, 경남) 제조업의 기업패널데이터(2000~06)를 사용하여 「4＋9 시·도 지역산업진흥사업」 가운데 정부의 기술개발과제 지원이 참여기업의 고용 및 경영 성과에 미치는 효과를 분석한다. 기술개발과제 참여의 순효과(ATT)를 선택편의 없이 추정하기 위해 성향점수짝짓기(PSM) 방법을 적용한다. 추정결과에 따르면, 동남권 지역산업진흥사업의 기술개발과제는 과제유형별로 정도의 차이는 있지만 과제종료 이후 수년에 걸쳐 참여기업의 고용과 R&D에 대체로 유의미한 정(+)의 효과를 미치는 것으로 나타났다.