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Wetlands play a vital role in hydrologic and ecologic communities. Since there are few studies conducted for wetland water level prediction due to the unavailability of data, this study developed a water level prediction model using various machine learning models such as artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machine (SVM). The Upo wetland, which is the largest inland wetland in South Korea, was selected as the study area. The daily water level gauge data from 2009 to 2015 were used as dependent variables, while the meteorological data and upstream water level gauge data were used as independent variables. Predictive performance evaluation using RF as the final model revealed 0.96 value for correlation coefficient (CC), 0.92 for Nash–Sutcliffe efficiency (NSE), 0.09 for root mean square error (RMSE), and 0.19 for persistence index (PI). The results indicate that the water level of the Upo wetland was well predicted, showing superior results compared to that of the ANN, which was used in a previous study. The results intend to provide basic data for development of a wetland management method, using water levels of previously ungauged areas. [ABSTRACT FROM AUTHOR]

Climate change and fast urbanization increased rainfall intensity and runoff discharge. These changes lead to the growing possibility of flood damages. In South Korea, a government established the "Comprehensive Flood Prevention Plan (CFPP)" for each of river basin against flood. The plan is based on the combination of structural measures selected by experienced hydrologists. However, it lacks clear criteria. To solving this problem, this study classifies the structural measures and suggests a combination method, which use relationships between measures, to ensure the objectivity of the process in identifying proper measures. For Anyangcheon river basin, two plans are provided; the first is developed by the study, and the other is by the CFPP. Comparing the two plans, the results show that the combination, selected by proposed method in this study, is economically more feasible compared to CFPP. Therefore, this study expected be useful when selecting and combining structural measures for formulating river basin flood prevention plans. [ABSTRACT FROM AUTHOR]

Recently, abnormal climate has been shown around the world by global warming and disasters are becoming more severe. In Korea, more than 90% of damages due to natural disasters is caused by typhoon and heavy rainfall storm and consecutive typhoons or heavy rainfalls had been occurred. Therefore, the studies for the flood damage due to the continuous typhoons or heavy rainfall events should be considered and we call mega flood here for this type of flood which occurs by the continuous hazards.In this study, we produced a continuous rainfall to create a mega rainfall scenario. Through this, we simulated flood discharge called mega flood and stage. Then we analyzed the inundation area by mega flood. Also we selected the appropriate locations which can be optimal shelters against mega flood using MultI-Criteria Decision Making (MCDM). The results of this study are expected to minimize human casualties from the mega flood and to be an important guideline for establishing nonstructural measures for mega flood which can be occurred in the future.Acknowledgment: This work is supported by the Korea Environmental Industry & Technology Institute(KEITI) grant funded by the Ministry of Environment(Grant 18AWMP-B083066-05). [ABSTRACT FROM AUTHOR]