• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.575-575
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• 2016

홍수로 인한 주거건물의 경제적 피해는 침수심, 침수기간, 유속 등의 재난인자와 제내지에 노출된 사회 경제적 인자들 간의 복합적인 작용으로 발생하지만, 다차원홍수피해산정법을 비롯한 대부분의 모형에서는 침수심 만을 고려하고 있다. 최근 국외에서는 홍수피해와 영향인자 간의 관계를 규명하여 홍수피해액 평가 시 침수심 외 중요한 기타 인자를 고려하는 연구가 시도된 바 있으나, 국내에서는 이것과 관련한 연구가 그동안 거의 진행된 바 없다. 이에 본 연구에서는 주거건물을 대상으로 홍수피해 영향인자를 규명하고자 과거 피해지역을 대상으로 설문조사를 시행하여 자료를 수집하였으며, 설문조사에 앞서 사전 문헌연구를 바탕으로 수리/수문학적 인자, 건물특성인자, 예방인자, 조기경보/조치인자, 사회 경제적인자, 피해인자로 설문항목을 크게 구분하여 설문지를 설계하였다. 설문조사는 2014년 8월에 광범위하게 피해가 발생한 부산광역시를 대상으로 1대1면접조사 방식으로 진행하였으며, 수집된 자료로부터 상관분석, 데이터마이닝 등의 통계적 분석을 통해 침수심 외 몇몇 중요한 피해액 영향인자를 확인할 수 있었다. 본 연구를 통해 평가된 홍수피해 영향인자는 향후 홍수피해평가 모델을 개발하거나 홍수피해저감을 위한 세부적인 계획에 기여할 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.188-188
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• 2018

최근 들어 우리나라에서는 봄부터 여름까지 가뭄과 폭염이 빈번하게 발생하고 있다. 가뭄 또는 폭염의 발생빈도가 높아질 것으로 예상되고 있는 바, 점차 사회, 경제적 피해 규모가 커질 것으로 예상된다. 따라서 가뭄 또는 폭염의 심도(severity)나 지속기간의 영향에 대한 분석을 통해 가뭄 또는 폭염의 위험도를 고려하여 대응할 필요가 있다. 가뭄의 경우에는 다양한 가뭄지수, 가뭄 빈도 및 심도 등에 대한 연구가 꾸준히 진행되고 있으나, 폭염에 대해서는 그러한 연구가 미비한 실정이다. 따라서 본 연구에서는 최근 우리나라에서 발생한 가뭄을 동반하는 폭염의 크기(magnitude)에 대한 분석을 수행하기 위해 폭염지수(heat wave magnitude index)를 산정하고자 한다. 일반적으로 우리나라 기상청에서는 폭염 주의보와 폭염 경보의 발령을 일최고기온이 각각 $33^{\circ}C$ 및 $35^{\circ}C$ 이상인 날이 2일 이상 지속될 것으로 예상되는 경우를 기준으로 하고 있다. 본 연구에서는 가뭄을 동반한 폭염 사상의 크기 분석을 위해 폭염이 지속되는 기간을 우리나라 기상청에서 정의하는 2일, 3일로 구분하여 적용하고, 폭염으로 정의하는 기준(threshold)의 경우는 기존의 $33^{\circ}C$ 및 $35^{\circ}C$와 함께 상대적인 기준을 적용하여 적용성을 알아보고자 한다. 적용 대상은 우리나라 종관기상관측소(ASOS)의 일최고기온 자료이며, 4월에서 10월 사이의 일최고기온을 대상으로 하였다. 이를 통해 폭염의 정도에 대해 정량화하고 이를 이용한 위험도 분석도 가능해지리라 판단된다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.299-304
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• 2006

This paper is for the on-line monitoring and controlling system in which remote central processor execute commands based on data transmitted via radio or cable captured from the industrial or marine environments. By executing the appropriate system commands, many mechanical parts in industrial environments and natural factors such as temperature and humidity are to be under control in the way of normal system condition. In this research, we control the temperature of a hydrochloric acid tank to be within the predetermined range by executing temperature controlling commands issued by remote central computer which decides the appropriate action for the total system based on the received sensor data transmitted via radio and cable media. This type of monitoring and controlling system has the various applications such as the disaster prevention system, ubiquitous embedded system, alarm system, and the USN systems.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.307-312
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• 2020

This study presents problems and improvement measures for the supply rate of single-alarm detector and fire extinguisher installed in households. Statistics from the NFDS show that 18 percent of all fires and 45 percent of deaths occurred in residential buildings over the past eight years. It was less than 60% that households be equipped rate of basic fire-fighting systems by 2019. In this study, I analyzed the law and statistics of fire to devise a method for fire safety. I proposed that the basic fire-fighting systems is be equipped in households. Like this : First, a free distribution policy for the over 60 years of age and Areas where is fire engine difficult to enter. Second, the policy of adopting safety pay in disaster. Third, the policy of expanding supply through the revision of the Licensed Real Estate Agents Act. Fourth, the policy of self-regulating installation by safety education and set up a data base system. Fifth make a law of household's National Fire Safety Standards.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.60-67
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• 2021

Purpose: Traditional markets often have irregular space utilization patterns because the spaces are created and divided as time passed. Internally, there is high risk of fire due to problems such as aging facilities and high-density of stores and externally, there is high risk of fire spread since it is often adjacent to deteriorated residential and commercial facilities. Method: In this paper, on-site investigations were carried out to check fire risk factors and fire spread risk, and fire occurrence and expansion risk factors were investigated for traditional markets in Hwanghak-dong and Dong-daemun by using large-scale fire data from existing traditional markets. Result: As a result of the analysis, there are likely to be various problems such as high fire load and lack of safety awareness due to aging facilities and high-density of stores. In particular, it is necessary to prepare countermeasures because deteriorated residential facilities with narrow alleys around traditional markets have high fire spread. It is situation that while traditional markets mainly are managing for fire and disaster centering on the merchant association, the surrounding residential areas are not properly managed. Conclusion: It is necessary to manage deteriorated residential facilities with traditional markets, also to be linked early warning system and information to evacuate rapidly in case of fire there.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.640-640
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• 2015

2010년과 2011년 서울에서 발생한 집중호우와 2014년 부산에서 발생한 집중호우의 발생으로 막대한 재산상의 피해와 사상자를 냈다. 2010년 9월 21일에 발생한 집중호우는 1908년 관측시작이래 가장 많은 비가 내린 것으로 기록되었으며 주거지 4,727호, 상가 1,164호, 공장 126동 등이 침수되고 13시를 기준으로 강서지점의 경우 시간당 98.5mm의 기록적인 강우를 기록하였으나, 관악지점은 5.5mm에 그쳐 두 지점간의 시간당 강우량의 편차가 약 200배 가까이 차이가 나는 것으로 나타났다. 이와 같이 최근 도시지역에서 국지성 집중호우가 증가하고 있으며 지역별 강우 편차가 크고 이에 따라 침수피해발생 여부도 지역에 따라 달라진다. 강수의 공간적 분포와 그로 인한 침수해석은 도시돌발홍수 예경보 시스템에 있어 무엇보다도 중요하다. 본 연구의 목적은 도시지역 돌발홍수 예경보 시스템 구축을 위한 정량적 강수추정 QPE(Quantitative Precipitation Estimation)기법에 따른 수리 수문학적 영향을 평가하는 것이다. 정량적 강수추정을 위해 AWS, SKP, 레이더 자료를 활용하여 250m의 해상도를 가지도록 크리깅을 적용하였다: QPE 1은 34개의 AWS의 지점우량을 지구통계학적 기법 중의 하나인 크리깅을 이용하여 산정한 기법, QPE 2는 AWS와 156개의 SKP의 강우데이터를 크리깅을 이용하여 산정한 기법, QPE 3는 광덕산 레이더를 이용한 기법, QPE 4는 AWS, SKP, 광덕산 레이더 자료를 조건부 합성한 기법이다. 월류량을 산정하기 위해 도시유출해석모형인 SWMM을 강남역 일대를 대상으로 구축하고 우수관로 시스템으로 유입되지 못한 노면류(Surface flow)를 함께 고려하였다. 침수해석을 위해서는 DHM모델을 적용하였으며 2013년 7월 기간에 발생한 호우에 대하여 분석을 수행하였다. 비교수행을 위해서 인접한 서초 AWS와 강남 AWS의 지점강우량도 함께 고려하였으며 모의결과를 국가 재난관리 정보 시스템(NMDS)에 침수피해가 확인된 가옥 및 빌딩 정보와 일치여부를 적합도로 산정하였다. 산정된 적합도를 통하여 정량적 강수추정기법에 따른 수리?수문학적 영향을 평가하였다. 실제 침수흔적정보와 비교 결과, QPE 2와 QPE4가 가장 적합도가 높았으며 이에 따라 고밀도의 관측망의 구성이 도시지역 침수해석결과에도 적합할 것으로 판단된다.

• Journal of Korea Water Resources Association
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• v.55 no.8
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• pp.565-575
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• 2022

Recently, in the field of water resource engineering, interest in predicting time series water levels and flow rates using deep learning technology that has rapidly developed along with the Fourth Industrial Revolution is increasing. In addition, although water-level and flow-rate prediction have been performed using the Long Short-Term Memory (LSTM) model and Gated Recurrent Unit (GRU) model that can predict time-series data, the accuracy of flow-rate prediction in rivers with rapid temporal fluctuations was predicted to be very low compared to that of water-level prediction. In this study, the Paldang Bridge Station of the Han River, which has a large flow-rate fluctuation and little influence from tidal waves in the estuary, was selected. In addition, time-series data with large flow fluctuations were selected to collect water-level and flow-rate data for 2 years and 7 months, which are relatively short in data length, to be used as training and prediction data for the LSTM and GRU models. When learning time-series water levels with very high time fluctuation in two models, the predicted water-level results in both models secured appropriate accuracy compared to observation water levels, but when training rapidly temporal fluctuation flow rates directly in two models, the predicted flow rates deteriorated significantly. Therefore, in this study, in order to accurately predict the rapidly changing flow rate, the water-level data predicted by the two models could be used as input data for the rating curve to significantly improve the prediction accuracy of the flow rates. Finally, the results of this study are expected to be sufficiently used as the data of flood warning system in urban rivers where the observation length of hydrological data is not relatively long and the flow-rate changes rapidly.

• The Journal of Bigdata
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• v.8 no.1
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• pp.99-109
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• 2023

Recently, not only in Korea but also around the world, we have been experiencing constant disasters such as typhoons, wildfires, and heavy rains. The property damage caused by typhoons and heavy rain in South Korea alone has exceeded 1 trillion won. These disasters have resulted in significant loss of life and property damage, and the recovery process will also take a considerable amount of time. In addition, the government's contingency funds are insufficient for the current situation. To prevent and effectively respond to these issues, it is necessary to collect and analyze accurate data in real-time. However, delays and data loss can occur depending on the environment where the sensors are located, the status of the communication network, and the receiving servers. In this paper, we propose a two-stage hybrid situation analysis and prediction algorithm that can accurately analyze even in such communication network conditions. In the first step, data on river and stream levels are collected, filtered, and refined from diverse sensors of different types and stored in a bigdata. An AI rule-based inference algorithm is applied to analyze the crisis alert levels. If the rainfall exceeds a certain threshold, but it remains below the desired level of interest, the second step of deep learning image analysis is performed to determine the final crisis alert level.

• Journal of Korea Water Resources Association
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• v.56 no.10
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• pp.619-629
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• 2023

Recent intensification of climate change has led to an increase in damages caused by droughts. Currently, in Korea, the Standardized Precipitation Index (SPI) is used as a criterion to classify the intensity of droughts. Based on the accumulated precipitation over the past six months (SPI-6), meteorological drought intensities are classified into four categories: concern, caution, alert, and severe. However, there is a limitation in classifying drought intensity solely based on precipitation. To overcome the limitations of the meteorological drought warning criteria based on SPI, this study collected emergency water supply damage data from the National Drought Information Portal (NDIP) to classify drought intensity. Factors of SPI, such as precipitation, and factors used to calculate evapotranspiration, such as temperature and humidity, were indexed using min-max normalization. Coefficients for each factor were determined based on the Genetic Algorithm (GA). The drought intensity based on emergency water supply was used as the dependent variable, and the coefficients of each meteorological factor determined by GA were used as coefficients to derive a new Drought Severity Classification Index (DSCI). After deriving the DSCI, cumulative distribution functions were used to present intensity stage classification boundaries. It is anticipated that using the proposed DSCI in this study will allow for more accurate drought intensity classification than the traditional SPI, supporting decision-making for disaster management personnel.

• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.