• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.845-857
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• 2020

Compound coastal disasters are a type of natural disaster featuring both internal and external flooding due to rises in sea-level, torrential rains, typhoons, and tsunamis. The, incidence and scale of damage from such disasters is increasing. This aim of this study was to review the current laws and systems managing the phenomenon of the coastal complex disaster, and to derive recommendations for improvements to manage and prevent them. In this study, the Framework Act on the Management of Disasters and Safety, the Countermeasures against Natural Disasters Act, the National Land Planning and Utilization Act, the Coast Management Act, the River Act, and the Sewerage Act were reviewed, with focus on the district-zoning system designated by ministries for the management of natural disasters along the coast. Through a comparison of the purpose and nature of the laws, spatial scope, and management resources, it was judged that it would be desirable to comprehensively manage compound coastal disasters based on the Countermeasures Against Natural Disasters Act. In order to overcome the limitations of the current system and to derive specific measures to improve laws and systems, a questionnaire survey on detailed factors was conducted targeting experts in natural disaster management. The results indicated that it is necessary to improve the current system or introduce a new system for the management of coastal complex disasters, with integrated management of land and sea areas through the installation and operation of integrated decision-making governance by related ministries such as MOIS, MOLIT, MOF, and ME.

• Korean Journal of Remote Sensing
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• v.38 no.6_3
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• pp.1761-1775
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• 2022

Due to unpredictable climate change, the frequency of occurrence of water-related disasters and the scale of damage are also continuously increasing. In terms of disaster management, it is essential to identify the damaged area in a wide area and monitor for mid-term and long-term forecasting. In the field of water disasters, research on remote sensing technology using Synthetic Aperture Radar (SAR) satellite images for wide-area monitoring is being actively conducted. Time-series analysis for monitoring requires a complex preprocessing process that collects a large amount of images and considers the noisy radar characteristics, and for this, a considerable amount of time is required. With the recent development of cloud computing technology, many platforms capable of performing spatiotemporal analysis using satellite big data have been proposed. Google Earth Engine (GEE)is a representative platform that provides about 600 satellite data for free and enables semi real time space time analysis based on the analysis preparation data of satellite images. Therefore, in this study, immediate water disaster damage detection and mid to long term time series observation studies were conducted using GEE. Through the Otsu technique, which is mainly used for change detection, changes in river width and flood area due to river flooding were confirmed, centered on the torrential rains that occurred in 2020. In addition, in terms of disaster management, the change trend of the time series waterbody from 2018 to 2022 was confirmed. The short processing time through javascript based coding, and the strength of spatiotemporal analysis and result expression, are expected to enable use in the field of water disasters. In addition, it is expected that the field of application will be expanded through connection with various satellite bigdata in the future.

• Journal of the Korean GEO-environmental Society
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• v.24 no.4
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• pp.5-11
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• 2023

Impulse waves generated by landslides near water bodies can lead to fatal damage to human life and surrounding infrastructure. These impulse waves are generally called landslide-impulsed waves and occur without being limited to a specific area. Recently, localized torrential rains have frequently occurred due to the influence of abnormal weather, both the frequency and scale of landslides occurring in Korea are increasing. Therefore, in this study, the experiments were conducted according to the mass ratio of the landslide models, and among the characteristics of the generated landslide-impulse waves. And the wave amplitude was observed and analyzed. In this study, a total of 75 experiments were conducted by repeating the experiment 5 times for 15 cases with mass ratios of 5 landslide models and 3 types of slope angles. As a result of experiments with different mass ratios of landslide models, if the landslides have the same initial energy, the size of the landslide-impulse waves generated by mixing granular and block forms is higher than the size of the landslide-impulse waves generated by pure granular and block landslides. It is analyzed that the size may be larger.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.386-388
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• 2021

According to data from the World Meteorological Organization (WMO) in 2019, the global average temperature between 2015 and 2019 increased by 1.1℃ compared to the pre-industrial period (1850-1900). If the average temperature rises by 1.5℃, the occurrence of natural disasters such as extreme high temperatures, heavy rains and droughts will increase, and this change will intensify depending on the speed and size of warming. Due to the effects of global warming, global surface temperatures have gradually risen, and tropical fruits, which could only be grown in tropical regions, can be seen in Korea. According to the 5th report released by the IPCC of the Intergovernmental Panel on Climate Change under the United Nations, the world's average temperature will rise 3.7 degrees Celsius at the end of the 21st century (2081-2100). If the temperature rises gradually, it is believed that Korea's current cultivation area, which can produce good quality fruit, could be turned into an unfavorable area in the future. This paper aims to develop a plant cultivation system that utilizes Arduino to provide a customized environment for the growth of plants desired by growers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.43-45
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• 2021

The short-term quantitative precipitation prediction (QPF) system is important socially and economically to prevent damage from severe weather. Recently, many studies for short-term QPF model applying the Deep Neural Network (DNN) has been conducted. These studies require the sophisticated pre-processing because the mistreatment of various and vast meteorological data sets leads to lower performance of QPF. Especially, for more accurate prediction of the non-linear trends in precipitation, the dataset needs to be carefully handled based on the physical and dynamical understands the data. Thereby, this paper proposes the following approaches: i) refining and combining major factors (weather radar, terrain, air temperature, and so on) related to precipitation development in order to construct training data for pattern analysis of precipitation; ii) producing predicted precipitation fields based on Convolutional with ConvLSTM. The proposed algorithm was evaluated by rainfall events in 2020. It is outperformed in the magnitude and strength of precipitation, and clearly predicted non-linear pattern of precipitation. The algorithm can be useful as a forecasting tool for preventing severe weather.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.385-385
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• 2021

하천에서 실제로 유속 2.0m/s 이상 발생할 시 유량측정은 매우 급변하는 유속과 수위변화에 따른 측정값의 불확실성, 운영적인 측면에서의 시·공간적 한계 등으로 고유량에 대해 정확한 유량을 산정하기 어려운 실정이다. 그리고 국가하천은 최소 80년 빈도 이상, 지방하천은 최소 50년 빈도 이상의 확률강우량 채택을 통해 고유량에 해당하는 계획홍수량을 산정하고 있으나, 실제로 높은 호우의 빈도는 쉽게 발생하지 않아 유량측정성과가 부재하거나 매우 극소수에 불과한 상황이다. 따라서 유량측정성과는 대상하천의 계획홍수량(계획홍수위) 이하의 수준, 즉 중규모 수위 이하의 구간에서 대부분의 성과를 가지고 있으므로 고유량 산정은 고수위 외삽추정식에 의존할 수밖에 없다. 고수위 외삽추정은 대체로 기 유량측정성과(h, q)와 통수단면적(AD^1/2) 자료를 이용하는 Stevens 방법을 주로 이용하며, 이 방법은 하폭에 비해 수심이 비교적 작은, 얕은 하천과 기 유량측정성과가 추정하려는 고수위 구간에 근접한 경우에 적용성이 매우 용이하다고 할 수 있다. 설마천 유역 전적비교 수위관측소의 경우는 수위 4.110m까지 최대로 통수할 수 있으며, 하폭은 24.230m, 관측 최고수위는 3.194m, 유량측정성과 최대수위는 1.613m(40.303m³/s)이다. 설마천 유역에 대해 Stevens 방법을 적용하는 경우 위 조건을 만족하지 않으므로 다른 방법으로의 접근이 필요하다. AMC-III 조건의 선행강수량과 지속기간 1시간을 갖는 최대강우강도별 관측도달시간 자료를 통해 관계식을 유도하였으며, 강우 빈도해석의 결과인 지속기간 1시간의 빈도별 강우강도에 해당하는 도달시간을 유속으로 환산하는 과정을 거쳤다. 그 결과 유속은 1.808m/s(2년 빈도_43.3mm)~4.254m/s(500년 빈도_101.9mm)이며, 기 유량측정성과의 결과인 수위, 통수단면적, 유속, 유량, 최대강우강도(86.1mm_80년 빈도)가 발생했을 때의 해당 유속(도달시간 환산값), 수위, 통수단면적을 통해 최종적으로 빈도(년)별 유속, 수위, 유량을 결정하였다. 한국하천일람(2018)에서 제시된 설마천 전체 유역의 80년 빈도 계획홍수량(315m³/s, A=17.59km²) 값은 전적비교 수위관측소(A=8.48km²)와 직접적인 비교는 어렵지만, 유역면적비(0.482)를 적용한 추정된 계획홍수량은 약 152m³/s 볼 수 있다. 상기의 빈도별 유속, 수위, 통수단면적 결과인 80년 빈도(86.1mm)-유속(3.594m/s)-수위(3.194m)-통수단면적(53.197m²)에 해당하는 계산된 유량은 191.212m³/s로 분석되었다. 그리고 최대통수가 가능한 수위 4.110m의 계산된 유량은 313.674m³/s(약 424년 빈도 추정, 유속 4.203m/s, 통수단면적 74.761m²)로 결국에는 빈도(년)에 해당하는 수위-유량관계식(고수위 외삽추정식)을 통해 고유량을 산정할 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.234-234
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• 2022

도로에서의 우수를 원활하게 처리하기 위해서 빗물받이 및 연결관 등의 노면 배수시설이 설치되고 있으며, 노면 배수는 측구부를 통해 흘러 빗물받이 유입부로 차집되고 연결관을 통해 하수관거로 배수된다. 그러나 최근 국내 기상패턴의 변화로 국지성 집중호우와 같이 시간당 강우량 증가로 도로부와 저지대에서 배수시설의 배수불량에 따른 도심지 내수침수 피해가 발생하고 있다. 이에 정부에서는 다양한 우수관거 개선사업, 빗물펌프장, 지하저류조와 같은 방재시설을 설치하고 있으나 우수유출저감시설은 대규모 예산이 소요되고 실제 침수지역에 피해 저감효과에 대한 효용성 문제에 대한 제기뿐만 아니라 과밀화된 도심지에서는 지하공간 활용에 한계가 있는 실정이므로 도심지의 다양한 공간을 활용한 도시 배수 및 저류시설에 대한 연구가 필요하다. 따라서 본 연구에서는 유휴 공간인 도로 측구부 공간을 활용하여 도로 노면수를 저류 및 지체할 수 있는 노면수 측구 저류시설의 개념을 제시하고 측구저류조의 활용성을 판단하기 위하여 빗물받이 유입구, 빗물받이, 측구 저류조 및 빗물받이와 측구저류조 연결부에서의 노면수 유입, 유출 및 저류 등의 다양한 흐름 변화를 확인하기 위하여 Fluent 모형의 적용성을 분석하였다. 수치모의 전체 형상은 50x50cm 크기의 빗물받이를 기준으로 양쪽에 2m 길이의 측구 저류조를 원형관으로 연결하여 1/5 축소모형으로 구성하고 격자는 빗물받이 유입부, 빗물받이 및 측구 저류조 내부의 복잡한 3차원 흐름을 모의하기 위해 사면체와 육면체로 조밀하게 생성하였다. 다상유동해석을 위해 VOF(Volume of Fluid)방법을 적용하였고, 수치해석 방법으로는 비정상류, 난류 모형으로는 SST k-ω모형을 적용하였다. 수치모의 조건으로는 설계빈도별(5~30년) 우수유출량을 산정하여 유입 유량별 기존 빗물받이 유입부에서의 유입흐름, 빗물받이 내부에서의 와 발생흐름, 측구 저류조 및 연결관에서의 흐름을 구현하여 분석하였다. 수치모의 결과 빗물받이 유입부에서 연결관을 통한 측구 저류조로 유입되는 유입흐름과 빗물받이 하단부의 배수관을 통해 유출되는 흐름을 정상적으로 구현하였으며, 빗물받이 유입부 및 측구 저류조 연결관에서의 유속변화도 확인할 수 있었다. 또한 빗물받이와 측구 저류조에서 다양한 흐름을 구현하기 위한 Flunet 모형의 적용성을 검토하였으며, 향후 수리실험을 통하여 실제 흐름과의 매개변수 최적화 및 다양한 도로 조건의 변화를 고려한 수치모의 분석을 통하여 지속적인 모형의 검증이 가능할 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.743-754
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• 2023

The global occurrence of myriad natural disasters and incidents, catalyzed by climate change and extreme meteorological conditions, has engendered substantial human and material losses. International organizations such as the International Charter have established an enduring collaborative framework for real-time coordination to provide high-resolution satellite imagery and geospatial information. These resources are instrumental in the management of large-scale disaster scenarios and the expeditious execution of recovery operations. At the national level, the operational deployment of advanced National Earth Observation Satellites, controlled by National Geographic Information Institute, has not only catalyzed the advancement of geospatial data but has also contributed to the provisioning of damage analysis data for significant domestic and international disaster events. This special edition of the National Disaster Management Research Institute delineates the contemporary landscape of major disaster incidents in the year 2023 and elucidates the strategic blueprint of the government's national disaster safety system reform. Additionally, it encapsulates the most recent research accomplishments in the domains of artificial satellite systems, information and communication technology, and spatial information utilization, which are paramount in the institution's disaster situation management and analysis efforts. Furthermore, the publication encompasses the most recent research findings relevant to data collection, processing, and analysis pertaining to disaster cause and damage extent. These findings are especially pertinent to the institute's on-site investigation initiatives and are informed by cutting-edge technologies, including drone-based mapping and LiDAR observation, as evidenced by a case study involving the 2023 landslide damage resulting from concentrated heavy rainfall.

• Journal of Wetlands Research
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• v.18 no.1
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• pp.24-31
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• 2016

The increase of impervious area in cities caused the unbalanced water cycle system and the accumulated various contaminants, which make troubles as introducing into watershed. In Korea, most of rainfall in a year precipitate in a summer season. This indicate that non-point source pollution control should be more important in summer and careful rainfall reuse strategy is necessary. Accordingly, the aim of this study is to monitor the characteristics of rainfall contaminants harvested in roofs and to develop the rainfall treatment system which are designed to fit well in a typical domestic household including rain garden. The rain garden consists of peatmoss, gravel and san to specially treat the initial rainfall contaminants. For this purpose, lab scale experiments with synthetic rainfall had been conducted to optimize the removal efficiency of TN, TP and CODcr. After lab scale experiments, field scale rainfall treatment system installed as a pilot scale in a field. This system has been monitored during June to July in 2015 in four time rainfall events as investigating the function of time, rainfall, and pollutant concentrations. As results, high loading of pollutants were introduced to the rainfall treatment system and its removal efficiency is increased as increase of pollutant concentrations. Since it is common that the mega-size of rainfall treatment system is not attractive in urban area, small scale rainfall treatment system is promising to treat the non-point source contaminants from cities. In addition, this small scale rainfall treatment system could have a potential to water resue system in islands, which usually suffer the shortage of water.