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박성천 ( Park Sung Chun ),문병석 ( Moon Byoung Seok ),오창주 ( Oh Chang Ju ),이병조 ( Lee Byoung Jo ) 한국농공학회 1998 韓國農工學會誌 : 전원과 자원 Vol.40 No.1
The purpose of this paper is to establish a method estimating the daily urban water demand using statistical analysis that is used for developing the efficient management and operation of the water supply facilities, and accurary of the model is verified by error rate and F-value. The data used in this study were the daily urban water use, the weather conditions such as temperature, precipitation, relative humidity, etc, and the day of the week. The case study was taken placed for the city of Namwon in Korea. The raw data used in this study were rearranged either by month or by season for analysis purpose, and the statistical analysis was applied to the data to obtain the regression model As a result of this study, the linear regression model was developed to estimate the daily urban water use with weather condition. The regression constant and coefficients of the model were determined for each month of a year. The accuracy of the model was within 3% of average error and within 11% of maximum error. The resulting model was found to be useful to the practical operation and management of the water supply facilities.
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.