• Journal of Korean Society of Disaster and Security
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• v.15 no.2
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• pp.13-25
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• 2022

Public consent is essential to proceed with large-scale projects such as dam and hydroelectric power plant in the Carbon Neutral Era. In general, when designing facilities such as dams and river facilities, the impact due to constructing them is analyzed through numerical simulation in advance. Those facilities are built to cope with floods and usually HEC-RAS is used for numerical simulation in this process. The numerical simulation provides accurate data, but it is very difficult to persuade the public only with the data. Therefore, this study intends to consider the utilization of metaverse in the field of urban flooding and flood response. The applicability of metaverse was confirmed by emphasizing visual effects and providing easy-to-see data, using a kind of metaverse platform called Cities: Skylines. The functions and limitations of this platform were reviewed. A virtual flood scenario was applied after implementing real cities on a metaverse. The hazard map established in Korea and the results of applying the scenario of metaverse platform were compared. On the metaverse, not only the disaster situation caused by realizing the city and society as it is, but also the spread of social disasters after the disaster can be confirmed. Through this, countermeasures can be virtually implemented. If these social and humanistic data are also verified in the future, it is expected that the overall process for responding to urban flooding can be modeled.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.181-196
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• 2019

Floods can be caused by a variety of factors, and the main cause of floods is the exceeding of urban drainage system or river capacity. In addition, rainfall frequently occurs that causes large watershed runoff. Since the existing methodology of preparing for flood risk map is based on hydraulic and hydrological modeling, it is difficult to analyse for a large area because it takes a long time due to the extensive data collection and complex analysis process. In order to overcome this problem, this study proposes a methodology of mapping for flood vulnerable area that considered the surface runoff mechanism. This makes it possible to reduce the time and effort required to estimate flood vulnerabilities and enable detailed analysis of large areas. The target area is Seoul, and it was confirmed that flood damage is likely to occur near selected vulnerable areas by verifying using 2×2 confusion matrix and ROC curve. By selecting and prioritizing flood vulnerable areas through the surface runoff mechanism proposed in this study, the establishment of systematic disaster prevention measures and efficient budget allocation will be possible.

• Journal of the Society of Disaster Information
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• v.11 no.1
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• pp.73-88
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• 2015

Due to the cases of recent global warming and unusual weather etc., large-scale natural disasters such as typhoons, floods, snow damage occur frequently across the continents such as Southeast Asia and North America, South America etc. and risks of earthquakes and tsunami are also increasing gradually in Korea which has been regarded as a safe zone and disaster types are also being diversified such as typhoons, floods, heat waves, heavy snow and damage scale is also enlarged. In addition, due to geographical characteristics or lack of infrastructure, disasters tended to occur intensively around a specific region or city in the past but disasters occur throughout the country in recent years so preparation for disaster prevention has emerged as an urgent challenge issue. Therefore, considering that the plan of obtaining the effective feedback function of disaster Information is very important in the proactive and software aspects for disaster reduction, this paper analyzed this three aspects of contents, procedural and contextual aspects and proposed the plan. First, in the content aspect, building disaster prevention information communication Infrastructure, building urban and regional disaster prevention system, obtaining concurrency and sharing of information and second, in the procedural aspect, active utilization of ICT(Information and Communication Technology) of the prevention stage, disaster prevention information collection and analysis reinforcement of the preparation stage, improvement of decision-making structure and field command system of the response stage, recovery system related information promotion of the recovery stage were proposed as alternatives and finally, in the contextual aspect, if disaster prevention information is effectively managed through maintenance of disaster prevention information related systems, obtaining domainality by disaster prevention work, improvement of the ability to judge the situation, obtaining comprehensive and feedback function etc, it is considered to significantly contribute to reducing natural disasters.

• Journal of Wetlands Research
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• v.22 no.3
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• pp.187-193
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• 2020

The increase in the area of impervious water due to the recent abnormal weather conditions and rapid urbanization led to a decrease in the amount of low current, resulting in an increase in the amount of surface runoff. Increased surface runoff is causing erosion, destruction of underwater ecosystems, human and property damage in urban areas due to flooding of urban river. The damage has been increasing in Korea recently due to localized heavy rains, typhoons and floods. As a countermeasure, the Busan Metropolitan Government will proceed with the creation of the Eco-Delta City waterfront zone in Busan with the aim of creating a future-oriented waterfront city from 2012 to 2023. Therefore, the current urban river conditions and precipitation data were collected by utilizing SWMM developed by the Environment Protection Agency, and the target basin was selected to simulate flood damage. Measures to reduce flood damage in various cases were proposed using simulated data. It is a method to establish a disaster prevention plan for each case by establishing scenario for measures to reduce flood damage. Considering structural and non-structural measures by performing an analysis of the drainage door with a 30-year frequency of 80 minutes duration, the expansion effect of the drainage pump station is considered to be greater than that of the expansion of the drainage door, and 8 scenarios and corresponding alternatives were planned in combination with the pre-excluding method, which is a non-structural disaster prevention measure. As a result of the evaluation of each alternative, it was determined that 100㎥/s of the pump station expansion and the pre-excluding EL.(-)1.5m were the best alternatives.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.49-56
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• 2023

Impact based forecast refers to providing information on potential socioeconomic risks according to weather conditions, away from the existing weather factor-oriented forecast. Developed weather countries are investing manpower and finances in technology development to provide and spread impact information, but awareness of impact based forecasts has not spread in Korea. In addition, the focus is on disasters such as floods and typhoons, which cause a lot of damage to impact based forecasts, and research on evaluating the impact of vehicle risks due to strong winds in the transportation sector with relatively low damage is insufficient. In Korea, there are not many cases of damage to vehicle conduction caused by strong winds, but there are cases of damage and the need for research is increasing. Road vulnerability is required to evaluate the risk of vehicles caused by strong winds, and the purpose of this study was to calculate the criteria for road vulnerability. The road vulnerability evaluation was evaluated by the altitude of the road, the number of lanes, the type of road. As a result of the analysis, it was found that the vulnerable area was well reproduced. It is judged that the results of this study can be used as a criterion for preparing an objective evaluation of potential risks for vehicle drivers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.2
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• pp.149-160
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• 2024

Severe casualties and property damage are occurring due to urban floods caused by extreme rainfall. However, there is a lack of research on preparedness, appropriate estimation of flood damages, assessment of losses, and compensation. Particularly, the flood damage estimation methods used in the USA and Japan show significant differences from the domestic situation, highlighting the need for methods tailored to the Korean context. This study addresses these issues by developing an optimized flood damage estimation technique based on the building characteristics. Utilizing the flood prediction solution developed by the Korea Institute of Science and Technology Information (KISTI), we have established an optimal flood damage estimation technology. We introduced a methodology for flood damage estimation by incorporating vulnerability curves based on the inventory of structures and apply this technique to real-life cases. The results show that our approach yields more realistic outcomes compared to the flood damage estimation methods employed in the USA and Japan. This research can be practically applied to procedures for flood damage in urban basement residences, and it is expected to contribute to establishing appropriate response procedures in cases of public grievances.

• Korean Journal of Remote Sensing
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• v.34 no.2_1
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• pp.301-306
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• 2018

Urbanization causes urban floods and urban heat island in the summer, so it is necessary to understanding the changes of the thermal environment through urban climate and energy balance. This can be explained by the energy balance, but in urban areas, unlike the typical energy balance, the storage heat flux saved in the building or artificial land cover should be considered. Since the environment of each city is different, there is a difficulty in applying the method of retrieving the storage heat flux of the previous research. Especially, most of the previous studies are focused on the overseas cities, so it is necessary to study the storage heat retrieval suitable for various land cover and building characteristics of the urban areas in Korea. Therefore, the object of this study, it is to derive the regression formula which can quantitatively retrieve the storage heat using the data of the area where various surface types exist. To this end, nonlinear regression analysis was performed using net radiation and surface temperature data as independent variables and flux tower based storage heat estimates as dependent variables. The retrieved regression coefficients were applied to each independent variable to derive the storage heat retrieval regression formula. As a result of time series analysis with flux tower based storage heat estimates, it was well simulated high peak at day time and the value at night. Moreover storage heat retrieved in this study was possible continuous retrieval than flux tower based storage heat estimates. As a result of scatter plot analysis, accuracy of retrieved storage heat was found to be significant at $50.14Wm^{-2}$ and bias $-0.94Wm^{-2}$.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.3
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• pp.273-283
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• 2020

Because of climate change, the occurrence of localized and heavy rainfall is increasing. It is important to predict floods in urban areas that have suffered inundation in the past. For flood prediction, not only numerical analysis models but also machine learning-based models can be applied. The LSTM (Long Short-Term Memory) neural network used in this study is appropriate for sequence data, but it demands a lot of data. However, rainfall that causes flooding does not appear every year in a single urban basin, meaning it is difficult to collect enough data for deep learning. Therefore, in addition to the rainfall observed in the study area, the observed rainfall in another urban basin was applied in the predictive model. The LSTM neural network was used for predicting the total overflow, and the result of the SWMM (Storm Water Management Model) was applied as target data. The prediction of the inundation map was performed by using logistic regression; the independent variable was the total overflow and the dependent variable was the presence or absence of flooding in each grid. The dependent variable of logistic regression was collected through the simulation results of a two-dimensional flood model. The input data of the two-dimensional flood model were the overflow at each manhole calculated by the SWMM. According to the LSTM neural network parameters, the prediction results of total overflow were compared. Four predictive models were used in this study depending on the parameter of the LSTM. The average RMSE (Root Mean Square Error) for verification and testing was 1.4279 ㎥/s, 1.0079 ㎥/s for the four LSTM models. The minimum RMSE of the verification and testing was calculated as 1.1655 ㎥/s and 0.8797 ㎥/s. It was confirmed that the total overflow can be predicted similarly to the SWMM simulation results. The prediction of inundation extent was performed by linking the logistic regression with the results of the LSTM neural network, and the maximum area fitness was 97.33 % when more than 0.5 m depth was considered. The methodology presented in this study would be helpful in improving urban flood response based on deep learning methodology.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.175-191
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• 2011

Colonization patterns and community changes in benthic macroinvertebrates in the Cheonggye Stream, a functionally restored stream in downtown Seoul, Korea, were studied from November 2005 to November 2007. Benthic macroinvertebrates were quantitatively sampled 15 times from five sites in the stream section. Taxa richness (59 species in total) increased gradually over the first year, whereas the density revealed seasonal differences with significantly lower values in the winter season and after flood events. The benthic macroinvertebrate fauna may have drifted from the upstream reaches during floods and from the Han River, arrived aerially, or hitchhiked on artificially planted aquatic plants. Oligochaeta, Chironommidae, Psychodidae, and Hydropsychidae were identified as major community structure contributors in the stream. Swimmers and clingers colonized relatively earlier in the upper and middle reaches, whereas burrowers dominated particularly in the lower reaches. Collector-gatherers colonized at a relatively early period throughout the stream reaches, and collector-filterers, such as the net-spinning caddisfly (Cheumatopyche brevilineata), predominated in the upper and middle reaches after a 1-year time period. Cluster analyses and multi-response permutation procedures demonstrated that the Cheonggye Stream shares more similarities with the Jungnang Stream than with the Gapyeong Stream. Detrended correspondence analysis and nonmetric multidimensional scaling demonstrated that physical environmental factors (depth, current velocity, dissolved oxygen, and pH) as well as nutrients (total nitrogen and total phosphorous), water temperature, and conductivity could affect the distribution of benthic macroinvertebrates in the study streams.

• Journal of KIISE
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• v.44 no.6
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• pp.607-612
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• 2017

This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity river, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time (RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.