• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2010.04a
• /
• pp.333-335
• /
• 2010

Global warming gas caused an increase in a direct and indirect problems like the rising sea level, seawater overflowing and a coastal flooding. The loss and damage of the republic of korea are increasing because of the rising sea level. As a result, It is necessary to establish the foundation of the monitoring of the sea level changes for the flooding prevention. A new measurement technique is developed using GPS equipped ship to make up for the spatial-temporal and economical problems by this study. We compared the data using GPS with the value for height of the tide. And we corrected the errors using the more accurate data that we studied. In addition to we studied that the corrected value had statistical significance and similarity compared with the observed value using GPS. The following studies also performed : When the observed value of tide by a tide observatory and by using GPS are applied to sounding ; How the values of the water depth are being, and if the values are similar, whether the observed value of tide using GPS is valid or not.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.23-30
• /
• 2021

In recent years, hazardous flash flooding has caused deaths and damage to infrastructure in Saudi Arabia. In this paper, our aim is to assess patterns and trends in climate means and extremes affecting flash flood hazards and water resources in Saudi Arabia for the purpose to improve risk assessment for forecast capacity. We would like to examine temperature, precipitation climatology and trend magnitudes at surface stations in Saudi Arabia. Based on the assessment climate patterns maps and trends are accurately used to identify synoptic situations and tele-connections associated with flash flood risk. We also study local and regional changes in hydro-meteorological extremes over recent decades through new applications of statistical methods to weather station data and remote sensing based precipitation products; and develop remote sensing based high-resolution precipitation products that can aid to develop flash flood guidance system for the flood-prone areas. A dataset of extreme events has been developed using the multi-decadal station data, the statistical analysis has been performed to identify tele-connection indices, pressure and sea surface temperature patterns most predictive to heavy rainfall. It has been combined with time trends in extreme value occurrence to improve the potential for predicting and rapidly detecting storms. A methodology and algorithms has been developed for providing a well-calibrated precipitation product that can be used in the early warning systems for elevated risk of floods.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2020.06a
• /
• pp.121-121
• /
• 2020

Varying dominant processes, including Tropical Cyclone (TC) and non-TC rainfall events, have been known to drive the occurrence of precipitation in South Korea. With the changes in the pattern of the Earth's climate due to anthropogenic activities, nonstationarity or changes in the magnitude and frequency of these dominant processes have been separately observed for the past decades and are expected to continue in the coming years. These changes often cause unprecedented hydrologic events such as extreme flooding which pose a greater risk to the society. This study aims to take into account a more reliable future climate condition with two dominant processes. Diverse statistical models including the hidden markov chain, K-nearest neighbor algorithm, and quantile mappings are utilized to mimic future rainfall events based on the recorded historical data with the consideration of the varying effects of TC and non-TC events. The data generated is then utilized to the hydrologic model to conduct a flood frequency analysis. Results in this study emphasize the need to consider the nonstationarity of design rainfalls to fully grasp the degree of future flooding events when designing urban water infrastructures.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.38 no.6
• /
• pp.839-850
• /
• 2018

The flood damage in urban areas due to torrential rain is increasing with urbanization. For this reason, accurate and rapid flooding forecasting and expected inundation maps are needed. Predicting the extent of flooding for certain rainfalls is a very important issue in preparing flood in advance. Recently, government agencies are trying to provide expected inundation maps to the public. However, there is a lack of quantifying the extent of inundation caused by a particular rainfall scenario and the real-time prediction method for flood extent within a short time. Therefore the real-time prediction of flood extent is needed based on rainfall-runoff-inundation analysis. One/two dimensional model are continued to analyize drainage network, manhole overflow and inundation propagation by rainfall condition. By applying the various rainfall scenarios considering rainfall duration/distribution and return periods, the inundation volume and depth can be estimated and stored on a database. The Rainfall-Duration-Flooding Quantity (RDF) relationship curve based on the hydraulic analysis results and the Self-Organizing Map (SOM) that conducts unsupervised learning are applied to predict flooded area with particular rainfall condition. The validity of the proposed methodology was examined by comparing the results of the expected flood map with the 2-dimensional hydraulic model. Based on the result of the study, it is judged that this methodology will be useful to provide an unknown flood map according to medium-sized rainfall or frequency scenario. Furthermore, it will be used as a fundamental data for flood forecast by establishing the RDF curve which the relationship of rainfall-outflow-flood is considered and the database of expected inundation maps.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.3
• /
• pp.316-320
• /
• 2008

Data dissemination schemes for wireless sensor networks, where sinks and event targets might be mobile, has been one of the active research fields. For doing that, stationary nodes gathered data on behalf of mobile sinks and the relayed data in previous studies. their schemes, however, lead to frequent query flooding and report congestion problems over sink moving. We propose a data dissemination protocol to solve both the query flooding and the report congestion problem. Our scheme improves the two shortcomings through sink location management. Finally, we prove effectiveness of our protocol through computer simulations.

• The KIPS Transactions:PartC
• /
• v.15C no.5
• /
• pp.351-358
• /
• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.771-783
• /
• 2023

This study aims to compare the performance of each machine learning model for preparing a grid-based disaster risk map related to flooding in Jung-gu, Ulsan, for Typhoon Chaba which occurred in 2016. Dynamic data such as rainfall and river height, and static data such as building, population, and land cover data were used to conduct a risk analysis of flooding disasters. The data were constructed as 10 m-sized grid data based on the national point number, and a sample dataset was constructed using the risk value calculated for each grid as a dependent variable and the value of five influencing factors as an independent variable. The total number of sample datasets is 15,910, and the training, verification, and test datasets are randomly extracted at a 6:2:2 ratio to build a machine-learning model. Machine learning used random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN) techniques, and prediction accuracy by the model was found to be excellent in the order of SVM (91.05%), RF (83.08%), and KNN (76.52%). As a result of deriving the priority of influencing factors through the RF model, it was confirmed that rainfall and river water levels greatly influenced the risk.

• Computers and Concrete
• /
• v.33 no.4
• /
• pp.425-443
• /
• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Journal of Korea Water Resources Association
• /
• v.53 no.12
• /
• pp.1183-1192
• /
• 2020

Economic analysis is a basic step in establishing disaster mitigation measures, but it is difficult to verify the results due to uncertainty. Therefore, the scope of investigation and analysis is wide. However, it is difficult to predict the amount of damage caused by flooding because the collection of relevant data is limited in the ungauged basin. In this study, distributed runoff analysis and flooding analysis were performed, and a method of estimating the amount of flood damage in the ungauged basin was proposed using collectible social and economic indicators and flood analysis results. For distributed runoff analysis and flooding analysis, GRM (Grid based Rainfall-runoff Model) and G2D (Grid based 2-Dimensional land surface flood model) developed by Korea Institute of Civil engineering and Building Technology were used. The method of substituting collectible social and economic indicators into the simple method and improvement method was used to estimate the amount of flood damage. As a result of the study, it was possible to estimate the amount of flood damage using satellite data and social and economic indicators in the ungauged basin.

• Journal of Korea Multimedia Society
• /
• v.11 no.2
• /
• pp.247-256
• /
• 2008

Recently, natural disasters like flooding damages have frequently occurred as to typhoons and local downpours affected by the climate changes. Many researches have actively been studied in analysing runoff models, the verification of their parameters, and the inflow on surfaces in order to lessen the damages. However, much time and effort needs in generating input files of the models in most current researches. Therefore, in this paper we develop a system for generating a simulation input data automatically. This system is connected to the EPA-SWMM based on the spatial data in the UIS systems and consists the simulation module for analysing urban flooding and the SWMM simulator module. Also, we construct a prototype using a range of regular inundation to generate a simulation input file. This system gives advantages showing inundation areas based on the map viewer as well as lessening errors of input data and simulation time.