Earticle

The National Assembly Budget Office of Korea was established 10 years ago, with the aim of supporting the budget deliberation process with expert analysis. This paper attempts to evaluate any impact that NABO has brought to the budget process of Korea. We examine the accuracy of the Administration’s macro forecasting used for the budget proposal as well as the amendment ratio of National Assembly’s budget deliberations, pre and post NABO’s establishment using simple regression model. Results show that the administration’s forecasting errors, macro or revenue, does not show any systematic decrease, while the total amendment ratio showed significant changes around the time when NABO was established. This indicates that an independent budget office’s existence can contribute to a thorough examination of a government’s budget, but more time is needed to gauge its impact clearly..

In this study, Mike11 will be used as the numerical model where a data assimilation method will be applied to it. This paper aims to gain an insight and understanding of data assimilation in flood forecasting models. It will start with a general discussion of data assimilation, followed by a description of the methodology and discussion of the statistical error forecast model used, which in this case is the linear regression. This error forecast model is applied to the water level forecast simulated by MIKE11 to produced improved forecast and validated against real measurements. It is found that there exists a phase error in the improved forecasts. Hence, 2 general formula are used to account for this phase error and they have shown improvement to the accuracy of the forecasts, where one improved the immediate forecast of up to 5 hours while the other improved the estimation of the peak discharge.

In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive particle swarm optimization (APSO) based on fitness function was put forward to optimize the kernel parameter σ and regularization parameter γ of LS-SVM. Based on the optimized forecasting model, non-parametric error correction model is also presented by iterative method. The error forecasted by non-parametric model was used to update the forecasted load so as to improve the forecasting accuracy. Load data selected from some area in South China as training and forecasting data is used to analyze. Case study illustrates that the proposed forecasting model (NP-APSO-SVM) has more generalized performance and better forecasting accuracy compared with the method of standard SVM.