• Title/Summary/Keyword: Data flooding

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Flooding Stress-Induced Glycine-Rich RNA-Binding Protein from Nicotiana tabacum

  • Lee, Mi-Ok;Kim, Keun Pill;Kim, Byung-gee;Hahn, Ji-Sook;Hong, Choo Bong
    • Molecules and Cells
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    • v.27 no.1
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    • pp.47-54
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    • 2009
  • A cDNA clone for a transcript preferentially expressed during an early phase of flooding was isolated from Nicotiana tabacum. Nucleotide sequencing of the cDNA clone identified an open reading frame that has high homology to the previously reported glycine-rich RNA-binding proteins. The open reading frame consists of 157 amino acids with an N-terminal RNA-recognition motif and a C-terminal glycine-rich domain, and thus the cDNA clone was designated as Nicotiana tabaccum glycine-rich RNA-binding protein-1 (NtGRP1). Expression of NtGRP1 was upregulated under flooding stress and also increased, but at much lower levels, under conditions of cold, drought, heat, high salt content, and abscisic acid treatment. RNA homopolymer-binding assay showed that NtGRP1 binds to all the RNA homopolymers tested with a higher affinity to poly r(G) and poly r(A) than to poly r(U) and poly r(C). Nucleic acid-binding assays showed that NtGRP1 binds to ssDNA, dsDNA, and mRNA. NtGRP1 suppressed expression of the fire luciferase gene in vitro, and the suppression of luciferase gene expression could be rescued by addition of oligonucleotides. Collectively, the data suggest NtGRP1 as a negative modulator of gene expression by binding to DNA or RNA in bulk that could be advantageous for plants in a stress condition like flooding.

Development of a method for urban flooding detection using unstructured data and deep learing (비정형 데이터와 딥러닝을 활용한 내수침수 탐지기술 개발)

  • Lee, Haneul;Kim, Hung Soo;Kim, Soojun;Kim, Donghyun;Kim, Jongsung
    • Journal of Korea Water Resources Association
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    • v.54 no.12
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    • pp.1233-1242
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    • 2021
  • In this study, a model was developed to determine whether flooding occurred using image data, which is unstructured data. CNN-based VGG16 and VGG19 were used to develop the flood classification model. In order to develop a model, images of flooded and non-flooded images were collected using web crawling method. Since the data collected using the web crawling method contains noise data, data irrelevant to this study was primarily deleted, and secondly, the image size was changed to 224×224 for model application. In addition, image augmentation was performed by changing the angle of the image for diversity of image. Finally, learning was performed using 2,500 images of flooding and 2,500 images of non-flooding. As a result of model evaluation, the average classification performance of the model was found to be 97%. In the future, if the model developed through the results of this study is mounted on the CCTV control center system, it is judged that the respons against flood damage can be done quickly.

Flooding Area Estimation by Using Different River Topographic Maps (하천지형 구축 방법에 따른 홍수 시 예상 침수면적 산정)

  • Moon, Changgeon;Lee, Jungsik;Shin, Shachul;Son, Hogeun
    • Journal of the Korean GEO-environmental Society
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    • v.17 no.9
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    • pp.21-28
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    • 2016
  • The purpose of this study is to compare the three areas that each estimated by using three different river topographic maps. For construction of river topographic maps, the data used in this study are ASTER, SRTM and a 1:5,000 scale digital map data sets in 14 streams of the Cheongdo-gun and Uiseong-gun. HEC-GeoRAS, RAS Mapper, and RiverCAD model are applied for the flooding area analysis using observed data and design rainfalls. The result of analysis is to compare observed flooding area based on the flood plain maps with estimated inundation area by hydraulic models and constructed river topographic maps. The results of this study are as follows; Flooding area by HEC-GeoRAS model is similar to the inundation area of flood plain map and appears in order of RAS Mapper, and RiverCAD model in all watersheds. Flood inundation area by SRTM DEM is similar to the result of 1:5,000 scale digital map in all watersheds and all analysis models. The SRTM DEM shows the most similarity to the digital map than ASTER DEM in all of the watershed scale and analysis models. HEC-GeoRAS and RiverCAD model are efficient models for flood inundation analysis in small watershed and HEC-GeoRAS and Ras Mapper model are efficient in medium to large watershed.

City Information Model-based Information Management of Flood Damages (도시정보모델의 침수피해정보관리에서의 활용)

  • Park, Sang Il;Kim, Min-Su;Kim, Jong Myung;Lee, Sang-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.28 no.4
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    • pp.385-392
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    • 2015
  • Open city information model can increase the understanding of the situation, enable the effective reuse of information due to access the semantic and relational conditions of objects, and support the reliable decision-making through linking with external references. The city information model focused on terrain and buildings was implemented based on the actual data. In addition, a process for flooding simulation was proposed using hydraulic analysis data and the city information model. The deaths and damages were estimated by flooding simulation. The availabilities were examined by detailed queries and responses based on model data of the city information model, hydraulic analysis data and the estimated damages.

WSN Data Dissemination Protocol by N-hop Access Guarantee Backbone (N홉 접근보장의 백본을 이용한 무선 센서 네트워크 데이터 전송 프로토콜)

  • Kim, Moon-Seong;Cho, Sang-Hun;Choo, Hyun-Seung
    • Journal of Internet Computing and Services
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    • v.10 no.1
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    • pp.43-50
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    • 2009
  • Flooding and SPIN, which are well-known WSN(Wireless Sensor Network) proactive protocols, spontaneously disseminate the sensed data without a request from an arbitrary sink node. However, these methods disseminate the data even to some nodes that do not need it, which is energy inefficient. In this paper, we introduce a semi-proactive protocol to disseminate only to pertinent nodes instead of all nodes in order to overcome this weakness. Thus some nodes, such as arbitrary sink nodes that need the sensed data, could easily obtain the data within some hops. The simulation result shows that the proposed protocol has higher average node energy efficiency than that of well-known earlier work, SPIN. If a proactive protocol, such as SPIN, is changed to semi-proactive and has only a 1-hop burden, then the energy efficiency enhancement is up to about 83% compared with SPIN.

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Operational Improvement of Small Urban Storm Water Pumping Station (1) - Simulation of Flood Hydrograph using GIS-based Hydrologic Model (도시 소유역 배수펌프장 운영개선 방안 연구 (1) - GIS 기반 수문모형에 의한 홍수유출수문곡선의 재현)

  • Gil, Kyung-Ik;Han, Jong-Ok;Kim, Goo-Hyun
    • Journal of Korean Society on Water Environment
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    • v.21 no.6
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    • pp.682-686
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    • 2005
  • Recently some urban areas have been flooded due to heavy storm rainfalls. Though major causes of these floodings may be attributed to localized heavy rainfalls, other factors are related to urban flooding including deficiency of storm sewer network capacity, change of surface runoff due to covered open channels, and operational problems of storm drainage pump stations. In this study, hydrologic and hydraulic analysis of Sutak basin in Guri city were carried out to evaluate flooding problems occurred during the heavy storm in July, 2001. ArcView, a world most widely used GIS tool, was used to extract required data for the hydrologic analysis including basin characteristics data, concentration times, channel routing data, land use data, soil distribution data and SCS runoff curve number generation from digital maps. HEC-HMS, a GIS-based runoff simulation model, was successfully used to simulate the flood inflow hydrograph to Sutak pumping station.

Applicability study on urban flooding risk criteria estimation algorithm using cross-validation and SVM (교차검증과 SVM을 이용한 도시침수 위험기준 추정 알고리즘 적용성 검토)

  • Lee, Hanseung;Cho, Jaewoong;Kang, Hoseon;Hwang, Jeonggeun
    • Journal of Korea Water Resources Association
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    • v.52 no.12
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    • pp.963-973
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    • 2019
  • This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.

Implementation of Flooding Routing Protocol for Field sever using Weather Monitoring System (국지기상 모니터링용 필드서버를 위한 플러딩 라우팅 프로토콜의 구현)

  • Yoo, Jae-Ho;Lee, Seung-Chul;Chung, Wan-Young
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.1
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    • pp.233-240
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    • 2011
  • A field server was developed by using ubiquitous sensor network technology to monitor the abrupt weather variation in local or mountain area. The data transmissions between deployed field servers in local terrain are very important technology in disaster prevention monitoring system. Weather related information such as temperature, humidity, illumination, atmospheric pressure, dew point and meteorological data are collected from the designated field at a regular interval. The received information from the multiple sensors located at the sensor field is used flooding routing protocol transmission techniques and the sensing data is transferred to gateway through multi-hop method. Telosb sensor node are programmed by nesC language in TinyOS platform to monitor the weather parameters of the local terrain.

Comparison of SAR Backscatter Coefficient and Water Indices for Flooding Detection

  • Kim, Yunjee;Lee, Moung-Jin
    • Korean Journal of Remote Sensing
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    • v.36 no.4
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    • pp.627-635
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    • 2020
  • With the increasing severity of climate change, intense torrential rains are occurring more frequently globally. Flooding due to torrential rain not only causes substantial damage directly, but also via secondary events such as landslides. Therefore, accurate and prompt flood detection is required. Because it is difficult to directly access flooded areas, previous studies have largely used satellite images. Traditionally, water indices such asthe normalized difference water index (NDWI) and modified normalized difference water index (MNDWI) which are based on different optical bands acquired by satellites, are used to detect floods. In addition, as flooding likelihood is greatly influenced by the weather, synthetic aperture radar (SAR) images have also been used, because these are less influenced by weather conditions. In this study, we compared flood areas calculated from SAR images and water indices derived from Landsat-8 images, where the images were acquired at similar times. The flooded area was calculated from Landsat-8 and Sentinel-1 images taken between the end of May and August 2019 at Lijiazhou Island, China, which is located in the Changjiang (Yangtze) River basin and experiences annual floods. As a result, the flooded area calculated using the MNDWI was approximately 21% larger on average than that calculated using the NDWI. In a comparison of flood areas calculated using water indices and SAR intensity images, the flood areas calculated using SAR images tended to be smaller, regardless of the order in which the images were acquired. Because the images were acquired by the two satellites on different dates, we could not directly compare the accuracy of the water-index and SAR data. Nevertheless, this study demonstrates that floods can be detected using both optical and SAR satellite data.