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인공신경망 이론을 이용한 홍수유출 예측시스템 개발 : GUI_FFS 개발 및 적용
박성천(Park Sung-Chun),오창열(Oh Chang-Ryol),김동렬(Kim Dong-Ryeol),진영훈(Jin Young-Hoon) 대한토목학회 2006 대한토목학회논문집 B Vol.26 No.2B
본 연구에서는 영산강 유역의 본류를 대표하는 나주지점과 황룡강 유역을 대표하는 선암지점에 대하여 물리적인 매개변수를 이용하지 않는 인공신경망 이론을 이용하여 강우-유출 과정의 비선형 모형을 개발하였다. 본 연구결과 나주지점에서는 ANN_NJ_9 모형이 선암지점에서는 ANN_SA_9 모형이 강우-유출 특성을 가장 잘 반영하였다. 또한, 본 연구에서 개발한 GUI_FFS에 대하여 기 확보된 강우 및 유출량을 적용한 결과 실측치와 예측치 간에 0.98이상의 R²값을 보임으로서 향후 수자원 및 하천계획 수립과 그에 따른 운영 및 관리에 효율성을 더할 수 있을 것이라 판단된다. In the present study, a nonlinear model of rainfall-runoff process using Artficial Neural networks(ANNs) which have no consideration on the physical parameter for the basin was developed at Naju station which is the main stream of Yeongsan-river, and Sunam station which is the main stream of Hwangryong-river. The result from the model of ANN_NJ_9 at the Naju station revealed the best result of the rainfall-runoff process, while the model of ANN_SA_9 for the Sunam station. Also, GUl_FFS developed in the research showed the R² of more than 0.98 between the observed and predicted values using the rainfall and runoff in the respective stations. Therefore, the GUl_FFS might be expected that it can playa role for the high reliability to operate and manage the water resources and the design of river plan more efficiently in the future.
박성천,오창열,김동렬 東新大學校 工業技術硏究所 2004 工業技術硏究 Vol.10 No.-
This research overcomes limitation of physical model using storage function method that is used in flood routing method of flood forecasting-warning system of 5 basic principle basin of our country. We also developed a nonlinear-model of rainfall-runoff process using artificial neural networks (ANNs) that do not consider to hydrology structure of basin. The results show that the ANN_SA_9 selected in the present study generally well-performed the one-step ahead prediction of runoff in the study area, the Sunam station. Also, base on ANN_SA_9 which have the value of R2 more than 0.95 developed in this search, we can have a higher reliability to operate and manage the water resources and rivers plan more efficiently in the future.
카오스이론을 이용한 일유출량 자료의 분석 및 예측에 관한 연구
박성천,오창열,김산원 東新大學校 工業技術硏究所 2004 工業技術硏究 Vol.10 No.-
This research studied about chaos characteristics analysis and forecasting in Youngsanpho. To find chaotic characteristic, daily runoff data of Youngsanpho were analyzed by correlation dimension method and lyapunov exponent method. To forecast daily streamflow, this study used the model joined Genetic Algorithm and Fuzzy System. So, we found the match valid out through the comparison of the observed values. From chaos analysis, chaotic characteristics were found in daily streamflow of Youngsanpho, and discovered consistently attracter in moving average data when it's reconstruction of Phase Space. Resulting from forecasting by using GA-Fuzzy System, short-term forecasting was excellent but long-term forecasting was impossible.