Bayesian changepoint analysis is applied to detect a change point in the 30-year (1976–2005) time series of the area-averaged annual maximum precipitation (A3MP) for the six accumulated time periods (1, 3, 6, 12, 24, and 48 h) over South Korea. Using noninformative priors, Bayesian model selection is performed by posterior probability through the Bayes factor, and the exact Bayes estimators of the parameters and unknown change point for the selected change model are obtained. To investigate the significance of the mean differences in the six A3MP between before and after the change point, posterior probability and 90% highest posterior density credible intervals are examined. The results show that a single change occurred around 1997 in the A3MP without regard to the accumulated time periods over South Korea. This is strongly consistent with the abrupt increases in the intensity and frequency of heavy precipitation after 1997. The A3MP after the change point (1997) significantly increased more than 15% compared with the A3MP before the change point. The intensification of A3MP resulted in a great increase of the annual total precipitation (about +18%), especially a greater increase of the heavy precipitation amount (+51%) and frequency (+48%) over South Korea.

Combined Cycle Power Plants (CCPPs) generate electrical power through gas turbines and use the exhaust heat from those turbines to power steam turbines, resulting in 50% more power output compared to traditional simple cycle power plants. Predicting the full-load electrical power output ($P_E$) of a CCPP is crucial for efficient operation and sustainable development. Previous studies have used machine learning models, such as the Bagging and Boosting models to predict $P_E$. In this study, we propose employing Super Learner (SL), an ensemble machine learning algorithm, to enhance the accuracy and robustness of predictions. SL utilizes cross-validation to estimate the performance of diverse machine learning models and generates an optimal weighted average based on their respective predictions. It may provide information on the relative contributions of each base learner to the overall prediction skill. For constructing the SL, we consider six individual and ensemble machine learning models as base learners and assess their performances compared to the SL. The dataset used in this study was collected over six years from an operational CCPP. It contains one output variable and four input variables: ambient temperature, atmospheric pressure, relative humidity, and vacuum. The results show that the Boosting algorithms significantly influence the performance of the SL in comparison to the other base learners. The SL outperforms the six individual and ensemble machine learning models used as base learners. It indicates that the SL improves the generalization performance of predictions by combining the predictions of various machine learning models.