Floods are natural disasters that should be considered a top priority in disaster management, and various methods have been developed to evaluate the risks. However, each method has different results and may confuse decision-makers in disaster management. In this study, a flood risk assessment method is proposed to integrate various methods to overcome these problems. Using factor analysis and principal component analysis (PCA), the leading indicators that affect flood damage were selected and weighted using three methods: the analytic hierarchy process (AHP), constant sum scale (CSS), and entropy. However, each method has flaws due to inconsistent weights. Therefore, a Bayesian network was used to present the integrated weights that reflect the characteristics of each method. Moreover, a relationship is proposed between the elements and the indicators based on the weights called the Integrated Index for Flood Risk Assessment (InFRA). InFRA and other assessment methods were compared by receiver operating characteristics (ROC)-area under curve (AUC) analysis. As a result, InFRA showed better applicability since InFRA was 0.67 and other methods were less than 0.5.

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.

As a significant portion of the available water resources in volcanic terrains such as Jeju Island are dependent on groundwater, reliable groundwater level forecasting is one of the important tasks for efficient water resource management. This study aims to propose deep-learning-based methods for groundwater level forecasting that can be utilized in actual management works and to assess their applicability. The study suggests practical forecasting methodologies through the Gyorae area of Jeju Island, where the groundwater level is highly volatile and unpredictable. To this end, the groundwater level data of the JH Gyorae-1 point and a total of 12 kinds of daily hydro-meteorological data from 2012 to 2021 were collected. Subsequently, five factors (i.e., mean wind speed, sun hours, evaporation, minimum temperature, and daily precipitation) were selected as hydro-meteorological data for groundwater level forecasting through cross-wavelet analysis between the collected hydro-meteorological data and groundwater level data. The study simulated the groundwater level of the JH Gyorae-1 point using the long short-term memory (LSTM) model, a representative deep-learning technique, with the selected data to show that the methodology is adequately applicable. In addition, for its better utilization in actual practice, the study suggests and analyzes (i) a derivatives-based groundwater level learning model which is defined as derivatives-based learning to forecast derivatives (gradients) of the groundwater level, not the target groundwater time series itself, and (ⅱ) an ensemble forecasting methodology in which groundwater level forecasting is performed repetitively with short time intervals.

The objective function is usually used for verification of the optimization process between observed and simulated flows for the parameter estimation of rainfall–runoff model. However, it does not focus on peak flow and on representative parameter for various rain storm events of the basin, but it can estimate the optimal parameters by minimizing the overall error of observed and simulated flows. Therefore, the aim of this study is to suggest the objective functions that can fit peak flow in hydrograph and estimate the representative parameter of the basin for the events. The Streamflow Synthesis And Reservoir Regulation (SSARR) model was employed to perform flood runoff simulation for the Mihocheon stream basin in Geum River, Korea. Optimization was conducted using three calibration methods: genetic algorithm, pattern search, and the Shuffled Complex Evolution method developed at the University of Arizona (SCE-UA). Two objective functions of the Sum of Squared of Residual (SSR) and the Weighted Sum of Squared of Residual (WSSR) suggested in this study for peak flow optimization were applied. Since the parameters estimated using a single rain storm event do not represent the parameters for various rain storms in the basin, we used the representative objective function that can minimize the sum of objective functions of the events. Six rain storm events were used for the parameter estimation. Four events were used for the calibration and the other two for validation; then, the results by SSR and WSSR were compared. Flow runoff simulation was carried out based on the proposed objective functions, and the objective function of WSSR was found to be more useful than that of SSR in the simulation of peak flow runoff. Representative parameters that minimize the objective function for each of the four rain storm events were estimated. The calibrated observed and simulated flow runoff hydrographs obtained from applying the estimated representative parameters to two different rain storm events were better than those retrieved from parameters estimated using a single rain storm event. The results of this study demonstrated that WSSR is adequate in peak flow simulation, that is, the estimation of peak flood runoff. In addition, representative parameters can be applied to a flow runoff simulation for rain storm events that were not involved in parameter estimation.

The inundation of urban areas has frequently occurred as a result of the localized heavy precipitation and flash floods in both South Korea and globally. Metropolitan areas with higher property value and population density than rural areas need practical strategies to reduce flood damage. Therefore, this study aims to perform an inundation analysis of coastal urban areas under a climate change scenario. Changwon city is one of the typical coastal metropolitan regions in South Korea. Severe flooding has occurred in this area caused by a combination of precipitation and sea-level rise enhanced by the typhoon, Sanba, in September 2012. At that time, daily precipitation was 65.5 mm, which is lower than the capable amount of rainfall of the drainage system. However, the river stage combined with the tidal wave caused by a typhoon and heavy precipitation exceeded the flood warning level. This study performed the flood inundation analysis for a part of Changwon city using the SWMM model, a two-dimensional urban flood analysis model. Furthermore, we considered the climate change scenarios to predict the potential flood damage that may occur in the future. As a result, as the future target period increases, both the flooding area and the inundation depth increase compared to the results of the inundation simulation according to the current precipitation and sea-level conditions. The inundation area increased by 2.6–16.2% compared to the current state, and the flooded depths would be higher than 1 m or more. We suggest a structural method to reduce inundation damages to consider extreme precipitation and tidal wave effects.

In recent decades, extreme storm events due to climate change have frequently occurred worldwide, a few of which have even occurred consecutively; we class such rainfall events as mega events. That is to say, if the inter-arrival time between rainfall events with a 100-year frequency is less than the IETD (Inter-Event Time Definition), the event can be considered a mega event. Therefore, the aim of this study was to implement flood inundation analysis using the hypothetical mega event from two consecutively occurring events of 100-year frequency, and select the optimal shelters using a developed method for minimizing casualties from floods. The Gyeongan stream basin, which is a tributary of the Namhan River in Korea, was selected as the study area. This study calculates mega flood discharge using the SSARR (Stream Synthesis and Reservoir Regulation) model, and conducts a flood inundation analysis of mega floods via the level pool method and the HEC-GeoRAS model. An inundation map was constructed, and the inundated area was classified into three zones and five administrative districts. Sixteen shelters were selected as candidates based on the criteria of the local government safety management plans and the Guidelines for Establishing the Disaster Relief Plan of 2013. To evaluate the candidates for evacuation in each district, we selected seven evaluation indicators from the shelter criteria of several countries, and calculated the weights of the indicators using the Analytic Hierarchy Process (AHP) method. As a result, four optimal shelters were selected in the study area. The results of the study can be used as the basic information for analyzing mega natural disaster events and inundation, and for establishing evacuation shelters, which are one of the non-structural flood protection measures.