수질 인자들은 다양하고 관계가 복잡하여 수질 변화를 예측하는데 많은 어려움이 있다. 따라서 입력과 출력이 비교적 용이하고 비선형 예측에 적합한 신경망 모형을 이용하여 금강유역 공주지점의 DO, BOD, TN에 대한 월수질 예측을 수행하고 ARIMA 모형과 비교하여 적용 가능성을 검토하였다. 사용된 신경망 모형은 학습을 위해 BP(Back Propagation) 알고리즘을 적용하였으며 학습을 향상시키기 위한 모멘트-적응학습율(Moment-Adaptive

The asphalt mixture with CRM(Crumb Rubber Modifier) is known to show a better performance in resisting thermal cracking, fatigue cracking and rutting compared with the conventional mixture. The laboratory tests on the physical characteristics of indirect tensile strength, density, flow and Marshall value of the CRM asphalt were conducted. The test results show that CRM asphalt has better physical characteristics than that of conventional asphalts. And the analysis on the noise reduction effect, penetration capacity from the field test on the national road in Haksan of Chungbuk, and recycling of tire waste were conducted. From this study, the results show that 1% CRM asphalt has higher the noise reduction effect and penetration capacity than those of conventional asphalts. And, optimal contents of crumb rubber modifier in the asphalt binder is one percent. In this case, crumb rubber modifier were used 10 ㎏ to make the asphalt binder of one cubic meter. So it was named as Eco-asphalt.

The aim of this study is to develop the water quality simulation model (BAYQUAL) that deal with the physical, chemical and biological aspects of fate/behavior of pollutants in the bay. BAYQUAL is a two dimensional, time-variable finite element water quality model based on the flow simulation model in bay(BAYFLOW). The algorithm is composed of a hydrodynamic module which solves the equations of motion and continuity, a pollutant dispersion module which solves the dispersion-advection equation. The applicability and feasibility of the model are discussed by applications of the model to the Kwangyang bay of south coastal waters of Korea. Based on the field data, the BAYQUAL model was calibrated and verified. The results were in good agreement with measured value within relative error of 14% for COD, T-N, T-P. Numerical simulations of velocity components and tide amplitude(M2) were agreed closely with the actual data.

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the most cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must build a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.

This study was carried out to develop the stream water quality model for the intaking station of Kongju waterworks in the Keum River system. The monthly water quality(total nitrogen and total phosphorus) with periodicity and trend were forecasted by multiplicative ARIMA models and then the applicability of the models was tested based on 7 years of the historical monthly water quality data at Kongju intaking site. The parameter estimation was made with the monthly observed data. The last one year data was used to compare the forecasted water quality by ARIMA model with the observed one. The models are ARIMA(2,0,0)×(0,1,1)_12 for total nitrogen, ARIMA(0,1,1)×(0,1,1)_12 for total phosphorus. The forecasting results showed a good agreement with the observed data. It is implying the applicability of multiplicative ARIMA model for forecasting monthly water quality at the Kongju site.