본 연구는 수력발전을 위한 저수지 관리에 있어 예측오차의 영향을 살펴보기 위해 예측오차를 Root Mean Square Error(RMSE)로 측정하였고, 이를 Generalized Maintenance Of Variance Extension (GMOVE)기법을 통하여 변화시켜보았다.변화된 예측오차의 RMSE는 천이확률을 통하여 Bayesian Stochastic Dynamic Programming (BSDP)에 고려되어졌으며, 이 BSDP 모형을 이용

Pusan is the largest coastal city with a population of about four million in Korea. Because of increased and confused traffic, photochemical air pollution become a major urban environmental problem recently. The photo-chemical air pollution weather forecasting method preciser than existing air pollution forecast method has been developed to forecast ozone episode days with meteorological conditions using the data measured at 7 air quality continuous monitoring stations from June to September using 2 years (1994, 1995). The method developed in present study showed higher percentage correct and skill score than existing air pollution forecasting in KMA (Korea Meteorological Administration).