• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.97-106
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• 2013

The temporal and spatial variations of water quality in a stratified reservoir are fully dependent on the characteristics of inflow loading from its watershed and the transport regimes of pollutants after entering the reservoir. Because of the meteorological and hydrological conditions in Korea, the pollutants loading to reservoirs are mostly occur during rainfall events. Therefore it is important to understand the characteristics of pollutants loading from upstream rivers and their spatial propagation through the stratified reservoir during the rainfall events. The objectives of this study were to characterize the water quality variations in upstream rivers of Imha Reservoir during a rainfall event, and the transport and spatial variations of pollutants in the reservoir through extensive field monitoring and laboratory analysis. The results showed that the event mean concentration (EMC) of SS, BOD, $COD_{Mn}$, T-N, T-P, $PO_4-P$ are 8.6 ~ 362.1, 2.5 ~ 5.1, 1.5 ~ 5.1, 1.1 ~ 1.9, 8.3 ~ 57.1, 5.6 ~ 25.7 times greater than the mean concentrations of these parameters during non-rainfall period. The turbidity and SS data showed good linear correlations, but the relationships between flow and SS showed large variations because of hysteresis effect during rising and falling periods of the flood. The ratio of POC to TOC were 12.6 ~ 14.7% during the non-rainfall periods, but increased up to 28.2 ~ 41.7% during the flood event. The turbid flood flow formed underflow and interflow after entering the reservoir, and delivered a great amount of non-point pollutants such as labile and refractory organic matters and nutrients to the metalimnion layer of reservoir, which is just above the thermocline. Spatially, the lateral variations of most water quality parameters were marginal but the vertical variations were significant.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.107-117
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• 2014

Hancheon reservoir, which is located upstream of Jeju city, has been built for flood mitigation after Typhoon Nari, 2007. To verify flood mitigating effect of the this reservoir on the downstream area, runoff analysis based on the measured data (two stream discharge monitoring stations and inflow data to the reservoir) is carried out during torrential rain followed by typhoon Dainmu, 2010. The stream water level was recorded as 3.14 m for the peak at the down gradient station. The stream water level under the assumption of absence of Hancheon reservoir is calculated as 4.16 m using the estimated rating curve, stream water propagation velocity, and the bypassed volume of water to the reservoir. This result shows that clear effect of reservoir operation which is capable of mitigating peak discharge in the downstream area.

• Journal of Korea Water Resources Association
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• v.48 no.12
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• pp.995-1009
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• 2015

Weather radar rainfall data has been recognized for making valuable contributions to short-term flood forecasting and management over the past decades. There are several advantages to better monitoring rainfall in ungauged area compared to ground-based rain gauges with which spatial patterns of the rainfall are not effectively identified. Hence, this study aims to develop a new scheme to forecast spatio-temporal rainfall field. The proposed model was based on an advection scheme to track wind patterns and velocity. The results showd a promising forecasting skill with quantitative and qualitative measures. It was confirmed that the forecasted rainfall may be effectively used an input data for a distributed hydrological model.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.47-52
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• 2003

Change detection and analysis is a powerful application of remote sensing, in that the spectral resolution of multi-band sensors can be used to advantage in monitoring both significant and subtle land cover changes over time. In this study, the LANDSAT TM data was used to detect the change areas affected by flood from a heavy rainfall. The study area is the Nakdong River located in the Korea peninsular. Among the several change detection techniques, change vector analysis(CVA), principle component analysis(PCA) and image difference approach are utilized in this paper. CVA uses any number of spectral bands from multi-date satellite data to produce change image that yield information of the magnitude and direction of differences pixel values. And accuracy assessment was carried out with a change image produced from three techniques. In result, CVA was found to be the most accurate for detecting areas affected by flood. CVA with the overall accuracy and Kappa coefficient of 97.27 percent and 94.45 percent, respectively.

• Journal of Korean Society of societal Security
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• v.2 no.4
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• pp.35-41
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• 2009

If any bridge has the monitoring system, the bridge manager can check the history of bridge behavior and the progress of the damage more exactly. When the unexpected event (ex: earthquake and flood) happens, the manager can check the safety condition of the bridge and make the pertinent action for bridge management which is reduction of vehicle speed or traffic control through the system. Additionary the manager can make the well-timed repair or reinforcement through the system, so he can save the management cost or the life cycle cost. This study presents the method of setting the criteria by FEM analysis in bridge monitoring system, and the standard progress for setting the criteria about measurement items of monitoring system for the steel box type bridge.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3B
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• pp.152-158
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• 2008

Recently, DDoS (Distributed Denial of Service) attack has emerged as one of the major threats and it's main characteristic is to send flood of data packets toward a specific victim. Thus, several attack detection schemes which monitor the destination IP address of packets have been suggested. The existing Bloom Filter based attack detection scheme is simple and can support real-time monitoring. However, since this scheme monitors the separate fields of destination IP address independently, wrong detection is comparatively high. In this paper, in order to solve this drawback, an efficient Bloom Filter based destination address monitoring scheme is proposed, which monitors not only separate fields but also relationship among separate fields. In the results of simulation, the proposed monitoring scheme outperforms the existing Bloom Filter based detection scheme. Also, to improve the correctness of detection, multi-layerd structure is proposed and the correctness of result is improved according to the number of layers and extra tables.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.751-754
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• 2008

Recently according to increasing of damage in creeks, it has been needed to develop a close-to-nature creek improvement technique considering flood safety as well as the functions of ecosystem and scenery preservation. In this study, the monitoring on creeks in seven provinces was accomplished to develop the technique applicable to domestic creeks between 2002 and 2007. Although the riparian ecosystem was disturbed, after just finishing the improvement on pilot creeks, the result of monitoring showed that restoration rates of creeks were satisfactory by constructing close-to-nature creek revetments. However it is hard to analyze on restoration and permissible tractive force of each revetments constructed in most creeks, because of insufficient monitoring data. Therefore the feasible study on the close-to-nature creek revetments should be performed in the near future through continuous monitoring on creeks.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.661-667
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• 2022

As remote sensing and GIS have been considered to be essential technologies for disasters information production, researches on developing methods for analyzing spatial data, and developing new technologies for such purposes, have been actively conducted. Especially, it is assumed that the use of remote sensing and GIS for disaster management will continue to develop thanks to the launch of recent satellite constellations, the use of various remote sensing platforms, the improvement of acquired data processing and storage capacity, and the advancement of artificial intelligence technology. This spatial issue presents 10 research papers regarding ship detection, building information extraction, ocean environment monitoring, flood monitoring, forest fire detection, and decision making using remote sensing and GIS technologies, which can be applied at the disaster prediction, monitoring and response stages. It is anticipated that the papers published in this special issue could be a valuable reference for developing technologies for disaster management and academic advancement of related fields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.288-290
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• 2022

As part of the SOC digitization for smart water management and flood prevention, the government reported that automatic and remote control system for drainage facilities (180 billion won) to 57% of national rivers and established a real-time monitoring system (30 billion won). In addition, they were also planning to establish a smart dam safety management system (15 billion won) based on big data at 11 regions. Therefore, research is needed for smart water management and flood prevention system that can accurately calculate the flow rate through real-time flow rate measurement of rivers. In particular, the most important thing to improve the system implementation and accuracy is to ensure the accuracy of real-time flow rate measurements. To this end, radar sensors for measuring the flow rate of electromagnetic waves in the United States and Europe have been introduced and applied to the system in Korea, but demand for improvement of the system continues due to high price range and performance. Consequently, we would like to propose an improved flow rate measurement and flood forecast system by developing a radar sensor for measuring the electromagnetic surface current meter for real-time flow rate measurement.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1095-1107
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• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.