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朴成天,表永平,李翰旼 동신대학교 환경연구소 1999 환경연구 Vol.4 No.1
본 연구는 영산강 유역의 지류 수질관리에 대한 본류의 수질을 예측하기 위한 다중선형회귀모형을 개발하기 위한 연구로써, 영산호의 수자원은 수질의 악화로 용수공급의 제한을 받고 있으므로 영산강 본류의 수질을 개선하고 보전하여 영산호의 수자원 이수범위를 확대시키고 수생태계의 복원에 기여하며, 양질의 수자원확보와 하천기능의 다양화에 대응하기 위한 기초연구이다. 영산강 중류부의 60km구간을 대상구간으로 설정하고 연구대상구간의 풍영정천, 광주천, 황룡강, 지석천, 만봉천, 문평천, 고막원천, 함평천의 지류를 대상으로 수질관리정도(BOD농도)와 청정수(회석수)의 유입율을 고려하여 목표년도 2001년의 영산강 본류의 수질을 QUAL2E모형에 적용하므로써 예측하였으며, 예측된 수질자료를 활용하여 각 지류의 수질관리에 대한 다중선형회귀모형을 개발하여 제시하였다. This study aimed for developing of the multiple linear regression model in the main stream of the Youngsan river. The extent of the water quality management and the inflow of clear water were applied to QUAL2E model as independent variables, and the predicted results of the QUAL2E model were used to develop the multiple linear regression model. Predictions of the water quality were carried out by QUAL2E model in the middle stream of the river extending over 60km. 2001year was selected as target year. The analysis of multiple linear regression at the middle and the bottom of the total reach was performed to predict the extent of the water quality management at tributary and the water quality in the main stream. According to the results, the water quality of the main stream was more affected by the extent of the water quality management at tributary than by the inflow rate. The conclusion proposed the regressive equation and the equation may be utilized to improve the water quality.
朴成天,文炳錫 東新大學校 工業技術硏究所 1999 工業技術硏究 Vol.5 No.-
The applicability of the system theoretic artificial neural network approach in developing effective nonlinear models of the rainfall-runoff process is demonstrated in this paper. The multi-layer networks, specially three-layer artificial neural networks have been used to model the rainfall-runoff process. And the data from the river Young-San has been utilized for the present study. This paper presents the potential of artificial neural network for simulating the hydrologic behavior of watersheds. The results of this paper suggest that the artificial neural network approach may provide a superior alternative to other methods of modeling for developing input-output simulation and forecasting models in situations that do not require modeling of the internal structure of the watershed.
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.
박성천,김용구 東新大學校 工業技術硏究所 2003 工業技術硏究 Vol.9 No.-
The basis of human life and being among the resources all of the earth is water and air. The water of modern city life is used to agricultural water, industrial water, production of energy and recreation. Also water was taken up unmoving position as natural resources. And it has been subject of boom just like a prevention of flood damage. Therefore Acquisition of discharge-data with confidence for effective management and division and rainfall-runoff relation of water resources as foundation work are very important. Discharge measurement of irrigation type is essential to pan of Dam, the improvement of river, construction plan of flooding-structure at river, establishment plan of water. And Flood control type is using to building of a bank, a flood system for prevention and warning. Both of them is important data. But Declination of discharge measurement data is very hard according to condition of weather, a river physical situation, a using situation of river water an upper stream and down stream. So it needs to verification. This study was selected a place of Naju at YoungSan river Andthen using HEC-RAS model. The result of correlation analysis about water-level of actual measurement and water-level ; the correlation coefficient is analyzed 0.998. In accordance with the result that confidence is recognized.
박성천,이관수 한국환경보건학회 1996 한국환경보건학회지 Vol.22 No.4
This research is to show the application of runoff model and runoff analysis of urban storm drainage network. the runoff models that were used for this research were RRL, ILLUDAS, and SWMM applicative object basin were Geucknak-chun and Sangmu drainage basin located in Seo-Gu, Kwangju. The runoff analysis employed the design storm that distributed the rainfall intensity according to the return period after the huff's method. The result from the comparative analysis of the three runoff models was as follows The difference of peak runoff by return period was 20-30% at Sangmu drainage area of $3.17 Km^2$, while less than 10% at Geucknak-chun drainage area of $12.7 Km^2$. The peak runoff were similar to all models. At the runoff hydrograph the times between rising and descending points were in the sequence of RRL, ILLUDAS and SWMM, but the peak times were similar to all models. The conveyance coefficient to examine the conveyance of the existing drainage network was 0.94-1.37, which means insecure, in Geucknak-chun drainage basin and 0.69-1.16, which means secure, in sangmu drainage basin.