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Abnormal weather conditions due to climate change are currently increasing on both global and local scales. It is therefore important to ensure the safety of the areas where major national facilities are located by analyzing risk quantitatively and re-evaluating the existing major facilities, such as nuclear power plants, considering the load and capacity of extreme flood conditions. In this study, a risk analysis method is developed that combines flood hazard curves with fragility curves using hydraulic and hydrological models by GIS tools and the @RISK model for the probabilistic flood analysis of nuclear power plant sites. A two-dimensional (2D) analysis is first carried out to estimate flood depths in various watershed scenarios, and a representative hazard curve for both external and internal flooding is made by applying a verified probability distribution type for the flood watersheds. For the analysis of flooding within buildings, an internal grid is constructed using GIS with related design drawings, and based on the flood depth results of the 2D analysis, a hazard curve for the representative internal inundation using a verified probability distribution type is presented. In the present study, walkdowns with nuclear experts are conducted around the nuclear power plant area to evaluate the fragile structures and facilities under possible flooding. After reviewing the 2D inundation analysis results based on the selected major equipment and facilities, the zones requiring risk assessment are re-assigned. A fragility curve applying probability distribution for the site’s major equipment and facilities is also presented. Failure risk analysis of the major facilities is then conducted by combining the proposed hazard and fragility curves. Results in the form of quantitative values are obtained, and the indicators for risks as well as the reliability and optimal measures to support decision-making are also presented. Through this study, it is confirmed that risk assessment based on the proposed probabilistic flood analysis technique is possible for flood events occurring at nuclear power plant sites.
Beom-Jin Kim; Minkyu Kim; Daegi Hahm; Junhee Park; Kun-Yeun Han. Probabilistic Flood Assessment Methodology for Nuclear Power Plants Considering Extreme Rainfall. Energies 2021, 14, 2600 .
AMA StyleBeom-Jin Kim, Minkyu Kim, Daegi Hahm, Junhee Park, Kun-Yeun Han. Probabilistic Flood Assessment Methodology for Nuclear Power Plants Considering Extreme Rainfall. Energies. 2021; 14 (9):2600.
Chicago/Turabian StyleBeom-Jin Kim; Minkyu Kim; Daegi Hahm; Junhee Park; Kun-Yeun Han. 2021. "Probabilistic Flood Assessment Methodology for Nuclear Power Plants Considering Extreme Rainfall." Energies 14, no. 9: 2600.
In recent years, the flooding risk of major facilities has increased significantly due to heavy rainfall events. These facilities must evaluate both external and internal flooding risk such as from heavy rainfall, flash floods, watershed flooding, river flooding, and coastal flooding. This study first estimates flooding from extreme rainfall with local intense precipitation and analyzes the resulting impacts on buildings and roads at a specific nuclear power plant (NPP) site, with which a roughness coefficient according to the landuse condition is estimated. A two-dimensional external flooding hazard analysis is then carried out with tidal levels as the external boundary conditions, and based on the results, new hazard curves for the inundation depth with frequency and duration are developed for the NPP site to show the relationships among rainfall, flood depth, and annual exceedance probability. To match the proper probability distribution types to the flood hazard curves, the fit was analyzed through Akaike’s information criterion verification. After analyzing the correlation between the flood depths, the mode values were calculated through Monte Carlo simulation with the verified probability distribution types. Finally, probabilistic flood hazard curves for the NPP site were obtained based on the calculated mode values. Representatively, when a 106-year return period rainfall occurs at the nuclear power plant site, the mode flood depth was found to be 1.07 m for power plant 1 and 0.61 m for power plant 2. In this way, the approach of this study is expected to support the waterproof design of critical facilities, flood prevention function design, the advancement of flood prevention measures and procedures, and the evaluation of flood mitigation functions.
Beom-Jin Kim; Minkyu Kim; Daegi Hahm; Kun Yeun Han. Probabilistic flood hazard assessment method considering local intense precipitation at NPP sites. Journal of Hydrology 2021, 597, 126192 .
AMA StyleBeom-Jin Kim, Minkyu Kim, Daegi Hahm, Kun Yeun Han. Probabilistic flood hazard assessment method considering local intense precipitation at NPP sites. Journal of Hydrology. 2021; 597 ():126192.
Chicago/Turabian StyleBeom-Jin Kim; Minkyu Kim; Daegi Hahm; Kun Yeun Han. 2021. "Probabilistic flood hazard assessment method considering local intense precipitation at NPP sites." Journal of Hydrology 597, no. : 126192.
An emergency action plan (EAP) for reservoirs and urban areas downstream of dams can alleviate damage caused by extreme flooding. An EAP is a disaster action plan that can designate evacuation paths for vulnerable districts. Generally, calculation of dam-break discharge in accordance with dam inflow conditions, calculation of maximum water surface elevation as per hydraulic channel routing, and flood map generation using topographical data are prepared for the purposes of creating an EAP. However, rainfall and flood patterns exhibited in the context of climate change can be extremely diverse. In order to prepare an efficient flood response, techniques should be considered that are capable of generating flood maps promptly while taking dam inflow conditions into account. Therefore, this study aims to propose methodology that is capable of generating flood maps rapidly for any dam inflow conditions. The proposed methodology was performed by linking a dynamic numerical analysis model (DAMBRK) with a random forest regression technique. The previous standard method of drawing flood maps often requires a significant amount of time depending on accuracy and personnel availability; however, the technique proposed here is capable of generating a flood map within one minute. Through use of this methodology, the time taken to prepare flood maps in large-scale water-disaster situations can be reduced. Moreover, methodology for estimating flood risk via use of flood mapping has been proposed. This study would provide assistance in establishing disaster countermeasures that take various flood scenarios into account by promptly providing flood inundation information to disaster-related agencies.
Hyun Kim; Kun Han. Linking Hydraulic Modeling with a Machine Learning Approach for Extreme Flood Prediction and Response. Atmosphere 2020, 11, 987 .
AMA StyleHyun Kim, Kun Han. Linking Hydraulic Modeling with a Machine Learning Approach for Extreme Flood Prediction and Response. Atmosphere. 2020; 11 (9):987.
Chicago/Turabian StyleHyun Kim; Kun Han. 2020. "Linking Hydraulic Modeling with a Machine Learning Approach for Extreme Flood Prediction and Response." Atmosphere 11, no. 9: 987.
For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions.
Yoon Ha Lee; Hyun Il Kim; Kun Yeun Han; Won Hwa Hong. Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment. Water 2020, 12, 2271 .
AMA StyleYoon Ha Lee, Hyun Il Kim, Kun Yeun Han, Won Hwa Hong. Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment. Water. 2020; 12 (8):2271.
Chicago/Turabian StyleYoon Ha Lee; Hyun Il Kim; Kun Yeun Han; Won Hwa Hong. 2020. "Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment." Water 12, no. 8: 2271.
To date, various methods of flood prediction using numerical analysis or machine learning have been studied. However, a methodology for simultaneously predicting the rainfall return period and an inundation map for observed rainfall has not been presented. Simultaneous prediction of the return period and inundation map would be a useful technique for responding to floods in real-time and could provide an expected inundation area by return period. In this study, return period estimation for observed rainfall was performed via PNN (probabilistic neural network). SVR (support vector regression) and a SOM (self-organizing map) were used to predict flood volume and inundation maps. The study area was the Gangnam area, which has experienced extensive urbanization. The database for training SVR and SOM was constructed by one- and two-dimensional flood analysis with consideration of 120 probable rainfall events. The probable rainfall events were composed with 2–100 year return periods and 1–3 hour durations. The SVR technique was used to predict flood volume according to the rainfall return period, and the SOM was used to cluster various expected flood patterns to be used for predicting inundation maps. The prediction results were compared with the simulation results of a two-dimensional flood analysis model. The highest fitness of the predicted flood maps in the study area was calculated at 85.94%. The proposed method was found to constitute a practical methodology that could be helpful in improving urban flood response capabilities.
Hyun Il Kim; Kun Yeun Han. Inundation Map Prediction with Rainfall Return Period and Machine Learning. Water 2020, 12, 1552 .
AMA StyleHyun Il Kim, Kun Yeun Han. Inundation Map Prediction with Rainfall Return Period and Machine Learning. Water. 2020; 12 (6):1552.
Chicago/Turabian StyleHyun Il Kim; Kun Yeun Han. 2020. "Inundation Map Prediction with Rainfall Return Period and Machine Learning." Water 12, no. 6: 1552.
Flooding due to the increase of heavy rainfall caused even larger damage in metropolitan areas. Therefore, numerical simulation and probabilistic models have been used for flood prediction, but the methodologies for real-time flood prediction by drainage district in metropolitan areas are still not sufficient. In this study, a flood scenario database was established by using one- and two-dimensional hydraulic analysis models to propose a realtime urban flood prediction method by drainage districts in metropolitan areas. Flood prediction models were constructed for each drainage district through the Nonlinear Auto-Regressive with eXogenous inputs and Self-Organizing Map (NARX-SOM). Suggested prediction model is a data-driven model because it is based on flood database which composed with diverse flood simulation results. To evaluate the predictive capacity of the models, flood prediction was performed for the actual heavy rainfall in 2010 and 2011 that caused severe flooding in Seoul, Republic of Korea. Flood prediction models for a total of 24 drainage districts were constructed, and it was found that the goodness of fit on the flood area ranged from 68.7 to 89.7%. In terms of the expected inundation map, the predictive power was found to be high when the SOM result with 5 × 5 dimension was mainly used. Through this study, it was possible to identify the predictive capability of the NARX-SOM flood prediction algorithm. The time for inundation map prediction for each area was within two minutes, but the one- and two-dimensional flood simulation usually takes 60–80 minutes. Moreover, when the calculated goodness of fit was examined, the proposed method was found to be a practical methodology that can be helpful in improving flood response capabilities.
Hyun Il Kim; Kun Yeun Han. Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction. KSCE Journal of Civil Engineering 2020, 24, 1932 -1943.
AMA StyleHyun Il Kim, Kun Yeun Han. Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction. KSCE Journal of Civil Engineering. 2020; 24 (6):1932-1943.
Chicago/Turabian StyleHyun Il Kim; Kun Yeun Han. 2020. "Data-Driven Approach for the Rapid Simulation of Urban Flood Prediction." KSCE Journal of Civil Engineering 24, no. 6: 1932-1943.
Data-driven models using an artificial neural network (ANN), deep learning (DL) and numerical models are applied in flood analysis of the urban watershed, which has a complex drainage system. In particular, data-driven models using neural networks can quickly present the results and be used for flood forecasting. However, not a lot of data with actual flood history and heavy rainfalls are available, it is difficult to conduct a preliminary analysis of flood in urban areas. In this study, a deep neural network (DNN) was used to predict the total accumulative overflow, and because of the insufficiency of observed rainfall data, 6 h of rainfall were surveyed nationwide in Korea. Statistical characteristics of each rainfall event were used as input data for the DNN. The target value of the DNN was the total accumulative overflow calculated from Storm Water Management Model (SWMM) simulations, and the methodology of data augmentation was applied to increase the input data. The SWMM is one-dimensional model for rainfall-runoff analysis. The data augmentation allowed enrichment of the training data for DNN. The data augmentation was applied ten times for each input combination, and the practicality of the data augmentation was determined by predicting the total accumulative overflow over the testing data and the observed rainfall. The prediction result of DNN was compared with the simulated result obtained using the SWMM model, and it was confirmed that the predictive performance was improved on applying data augmentation.
Hyun Il Kim; Kun Yeun Han. Urban Flood Prediction Using Deep Neural Network with Data Augmentation. Water 2020, 12, 899 .
AMA StyleHyun Il Kim, Kun Yeun Han. Urban Flood Prediction Using Deep Neural Network with Data Augmentation. Water. 2020; 12 (3):899.
Chicago/Turabian StyleHyun Il Kim; Kun Yeun Han. 2020. "Urban Flood Prediction Using Deep Neural Network with Data Augmentation." Water 12, no. 3: 899.
This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small–medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed rainfall and water level without extensive physical and topographic data, and can be performed in real-time by integrating point- and section flood forecasting and spatial inundation analysis.
Byunghyun Kim; Seng Yong Choi; Kun-Yeun Han; Kim; Choi; Han. Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams. Water 2019, 11, 919 .
AMA StyleByunghyun Kim, Seng Yong Choi, Kun-Yeun Han, Kim, Choi, Han. Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams. Water. 2019; 11 (5):919.
Chicago/Turabian StyleByunghyun Kim; Seng Yong Choi; Kun-Yeun Han; Kim; Choi; Han. 2019. "Integrated Real-Time Flood Forecasting and Inundation Analysis in Small–Medium Streams." Water 11, no. 5: 919.
This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood index predicted from the 2-D flood analysis based on several scenarios was fuzzified to reflect the uncertainties of the indices. The fuzzified flood indices were integrated using the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), resulting in a single graded flood hazard map. This methodology was applied to the Gam river in South Korea and confirmed that the Fuzzy MCDM (Multiple Criteria Decision Making) technique can be used to produce flood hazard maps. The flood hazard map produced in this study compared with the current flood hazard map of MOLIT (Ministry of Land, Infrastructure and Transports). This study found that the proposed methodology was more advantageous than the current methods with regard to the accuracy and grading of the flood areas, as well as in regard to an integrated single map. This report is expected to be expand upon other floods, including dam failure and urban flooding.
Tae Hyung Kim; Byunghyun Kim; Kun-Yeun Han. Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure. Water 2019, 11, 592 .
AMA StyleTae Hyung Kim, Byunghyun Kim, Kun-Yeun Han. Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure. Water. 2019; 11 (3):592.
Chicago/Turabian StyleTae Hyung Kim; Byunghyun Kim; Kun-Yeun Han. 2019. "Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure." Water 11, no. 3: 592.
Damage caused by flash floods is increasing due to urbanization and climate change, thus it is important to recognize floods in advance. The current physical hydraulic runoff model has been used to predict inundation in urban areas. Even though the physical calculation process is astute and elaborate, it has several shortcomings in regard to real-time flood prediction. The physical model requires various data, such as rainfall, hydrological parameters, and one-/two-dimensional (1D/2D) urban flood simulations. In addition, it is difficult to secure lead time because of the considerable simulation time required. This study presents an immediate solution to these problems by combining hydraulic and probabilistic methods. The accumulative overflows from manholes and an inundation map were predicted within the study area. That is, the method for predicting manhole overflows and an inundation map from rainfall in an urban area is proposed based on results from hydraulic simulations and uncertainty analysis. The Second Verification Algorithm of Nonlinear Auto-Regressive with eXogenous inputs (SVNARX) model is used to learn the relationship between rainfall and overflow, which is calculated from the U.S. Environmental Protection Agency’s Storm Water Management Model (SWMM). In addition, a Self-Organizing Feature Map (SOFM) is used to suggest the proper inundation area by clustering inundation maps from a 2D flood simulation model based on manhole overflow from SWMM. The results from two artificial neural networks (SVNARX and SOFM) were estimated in parallel and interpolated to provide prediction in a short period of time. Real-time flood prediction with the hydraulic and probabilistic models suggested in this study improves the accuracy of the predicted flood inundation map and secures lead time. Through the presented method, the goodness of fit of the inundation area reached 80.4% compared with the verified 2D inundation model.
Hyun Il Kim; Ho Jun Keum; Kun Yeun Han. Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods. Water 2019, 11, 293 .
AMA StyleHyun Il Kim, Ho Jun Keum, Kun Yeun Han. Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods. Water. 2019; 11 (2):293.
Chicago/Turabian StyleHyun Il Kim; Ho Jun Keum; Kun Yeun Han. 2019. "Real-Time Urban Inundation Prediction Combining Hydraulic and Probabilistic Methods." Water 11, no. 2: 293.