Typhoons can often cause inundation in lower coastal cities by inducing strong surges and waves. Being affected by typhoon annually, the coastal cities in South Korea are very vulnerable to typhoons. In 2016, a typhoon ‘CHABA’, with a maximum 10 min sustained wind speed of about 50 m/s and a minimum central pressure of 905 hPa, hit South Korea, suffering tremendous damage. In particular, ‘CHABA’-induced coastal inundation resulted in serious damage to the coastal area of Busan where a lot of high-rise buildings and residential areas are concentrated, and was caused by the combined effect of tide, surge, and wave. The typhoon-induced surge raised sea levels during high tide, and the strong wave with a long period of more than 10 s eventually led to the coastal inundation at the same time. The present research focuses a numerical downscaling considering the effects of tide, surge and wave for coastal inundation induced by Typhoon ‘CHABA’. This downscaling approach applied several numerical models, which are the Weather Research and Forecasting model (WRF) for typhoon simulation, the Finite Volume Community Ocean Model (FVCOM) for tide and surge simulation, and the Simulating WAve Nearshore (SWAN) for wave simulation. In a domain covering the Korean Peninsula, typhoon-induced surges and waves were simulated applying the results simulated by WRF as meteorological conditions. In the downscaled domain ranged near the coastal area of Busan, the coastal inundation was simulated blending a storm tide height and an irregular wave height obtained from the domain, in which each height has 1 s interval. The irregular wave height was calculated using the significant wave height and peak period. Through this downscaling study, the impact of storm tide and wave on coastal inundation was estimated.

Recent studies have focused on using big-data-based machine learning to address the effects of climate change scenarios on the production and quality of medicinal plants. Challenges relating to data collection can hinder the analysis of key feature variables that affect the quality of medicinal plants. In the study presented herein, we analyzed feature variables that affect the phenolic content of Korean Cnidium officinale Makino (C. officinale Makino) under different climate change scenarios. We applied different climate change scenarios based on environmental information obtained from Yeongju city, Gyeongsangbuk-do, Republic of Korea, and cultivated C. officinale Makino to collect data. The collected data included 3237, 75, and 45 records, and data augmentation was performed to address this data imbalance. We designed a function based on the DPPH value to set the phenolic content grade in C. officinale Makino and proposed a stacking ensemble model for predicting the total phenol contents and classifying the phenolic content grades. The regression model in the performance evaluation presented an improvement of 6.23–7.72% in terms of the MAPE; in comparison, the classification model demonstrated a 2.48–3.34% better performance in terms of accuracy. The classification accuracy was >0.825 when classifying phenol content grades using the predicted total phenol content values from the regression model, and the area under the curve values of the model indicated high model fitness (0.987–0.981). We plan to identify the key feature variables for the optimal cultivation of C. officinale Makino and explore the relationships among these feature variables.

The authors wish to make the following corrections to this paper