Seasonal forecasting has numerous socioeconomic benefits because it can be used for disaster mitigation. Therefore, it is necessary to diagnose and improve the seasonal forecast model. Moreover, the model performance is partly related to the ocean model. This study evaluated the hindcast performance in the upper ocean of the Global Seasonal Forecasting System version 5-Global Couple Configuration 2 (GloSea5-GC2) using a multivariable integrated evaluation method. The normalized potential temperature, salinity, zonal and meridional currents, and sea surface height anomalies were evaluated. Model performance was affected by the target month and was found to be better in the Pacific than in the Atlantic. An increase in lead time led to a decrease in overall model performance, along with decreases in interannual variability, pattern similarity, and root mean square vector deviation. Improving the performance for ocean currents is a more critical than enhancing the performance for other evaluated variables. The tropical Pacific showed the best accuracy in the surface layer, but a spring predictability barrier was present. At the depth of 301 m, the north Pacific and tropical Atlantic exhibited the best and worst accuracies, respectively. These findings provide fundamental evidence for the ocean forecasting performance of GloSea5.

PURPOSES :This study aims to improve complex modeling of multivariable, nonlinear, and overdispersion data with an artificial neural network that has been a problem in the civil and transport sectors.METHODS :Deep learning, which is a technique employing artificial neural networks, was applied for developing a large bus fuel consumption model as a case study. Estimation characteristics and accuracy were compared with the results of conventional multiple regression modeling.RESULTS :The deep learning model remarkably improved estimation accuracy of regression modeling, from R-sq. 18.76% to 72.22%. In addition, it was very flexible in reflecting large variance and complex relationships between dependent and independent variables.CONCLUSIONS :Deep learning could be a new alternative that solves general problems inherent in conventional statistical methods and it is highly promising in planning and optimizing issues in the civil and transport sectors. Extended applications to other fields, such as pavement management, structure safety, operation of intelligent transport systems, and traffic noise estimation are highly recommended.

최적제어이론은 -6유에서 무한대까지의 게인여유와 60˚의 위상여유, 그리고 원조건(Circle Condition)에 의해 보증되는 감도감소 등과 같은 로바스트성에 대한 긍정적인 면들을 가지고 있다. 이러한 결과는 전상태 피드백 경우일 때는 타당하지만, 실현문제에 있어, 관측기나 칼만필터가 사용되어질 경우에는 위의 조건이 완전히 만족하지 않는다. 본 논문에서는 관측기를 이용하여 다변수 제어계를 설계할 경우, 입「출력부분에서 안정여유가 회복될 수 있도록 하는 하나의 방법을 제안했다. 플랜트 입력과 출력에서 LQ가 보증하는 최소안정여유는 K=1의 경우에서도 회복될 수 있음을 보였다. 로바스트성 회복을 위해 본 논문에서는 종래의 방법(K=∞의 조건)과는 달리 유한값으로 하중행렬이 주어질 수 있으므로 본 방법은 하나의 명료한 로바스트성 회복법이라 할 수 있다. F=MC(or K=BM)를 만족하는 행렬 M이 존재하지 않을 경우, 그 해는 얻어질 수 없다. 그러나 K를 유한값으로 취했을 경우, 이 방법 이외의 또 다른 방법으로서는 입력과 출력에서 로바스트성 회복을 가지게 할 수 없을 것으로 생각된다.