• Smart Structures and Systems
• /
• v.15 no.6
• /
• pp.1583-1600
• /
• 2015

When natural disasters occur, including earthquakes, tsunamis, and debris flows, they are often accompanied by various types of damages such as the collapse of buildings, broken bridges and roads, and the destruction of natural scenery. Natural disaster detection and warning is an important issue which could help to reduce the incidence of serious damage to life and property as well as provide information for search and rescue afterwards. In this study, we propose a novel computer vision technique for debris flow detection which is feature-based that can be used to construct a debris flow event warning system. The landscape is composed of various elements, including trees, rocks, and buildings which are characterized by their features, shapes, positions, and colors. Unlike the traditional methods, our analysis relies on changes in the natural scenery which influence changes to the features. The "background module" and "monitoring module" procedures are designed and used to detect debris flows and construct an event warning system. The multi-criteria decision-making method used to construct an event warring system includes gradient information and the percentage of variation of the features. To prove the feasibility of the proposed method for detecting debris flows, some real cases of debris flows are analyzed. The natural environment is simulated and an event warning system is constructed to warn of debris flows. Debris flows are successfully detected using these two procedures, by analyzing the variation in the detected features and the matched feature. The feasibility of the event warning system is proven using the simulation method. Therefore, the feature based method is found to be useful for detecting debris flows and the event warning system is triggered when debris flows occur.

• 한국정보통신설비학회:학술대회논문집
• /
• 2009.08a
• /
• pp.157-161
• /
• 2009

In recent years, there is a remarkable progress in ICTs (Information and Communication Technologies), and then many attempts to apply ICTs to other industries are being made. In the field of disaster managements, ICTs such as RFID (Radio Frequency IDentification) and USN (Ubiquitous Sensor Network) are used to provide safe environments. Actually, various types of early warning systems using USN are now widely used to monitor natural disasters such as floods, landslides and earthquakes, and also to detect human-caused disasters such as fires, explosions and collapses. These early warning systems issue alarms rapidly when a disaster is detected or an event exceeds prescribed thresholds, and furthermore deliver alarm messages to disaster managers and citizens. In general, these systems consist of a number of various sensors and measure real-time stream data, which requires an efficient and rapid data processing technique. In this study, an event-driven architecture (EDA) is presented to collect event effectively and to provide an alert rapidly. A publish/subscribe event processing method to process simple event is introduced. Additionally, a complex event processing (CEP) technique is introduced to process complex data from various sensors and to provide prompt and reasonable decision supports when many disasters happen simultaneously. A basic concept of CEP technique is presented and the advantages of the technique in disaster management are also discussed. Then, how the main processing methods of CEP such as aggregation, correlation, and filtering can be applied to disaster management is considered. Finally, an example of flood forecasting and early alarm system in which CEP is incorporated is presented It is found that the CEP based on the EDA will provide an efficient early warning method when disaster happens.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.332-334
• /
• 2015

Nowadays a disaster event such as a building on fire, an earthquake or typhoon could occur any time, and any where. In such event, a warning notification system is a vital tool to send warning notifications to at-risk people in advance and provide them useful information to escape the dangerous area. Though some systems have been proposed such as emergency alert system using android, SMS or P2P overlay network, these works mainly focus on a reliable message distribution methods. In this work, we introduce a full prototype implementation of a personalized warning notification system based on geosocial information, which generates a personalized warning message for each user and delivers the messages through email or an android application. The system consists of four main modules: a web interface, database, a knowledge-based message generator, and message distributor. An android application is also created for user to receive warning messages on their smart phone. The prototype has been demonstrated successfully with a building-on-fire scenario.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.141-141
• /
• 2021

This study proposed an equation for Rainfall Threshold for Flood Warning (RTFW) for urban areas based on computer simulations. First, a coupled 1D-2D dual-drainage model was developed for nine watersheds in Seoul, Korea. Next, the model simulation was repeated for a total of 540 combinations of the synthetic rainfall events and watershed imperviousness (9 watersheds × 4 NRCS Curve Number (CN) values × 15 rainfall events). Then, the results of the 101 simulations with the critical flooded depth (0.25m-0.35m) were used to develop the equation that relates the value of RTFW to the rainfall event temporal variability (represented as coefficient of variation) and the watershed Curve Number. The results suggest that 1) the rainfall with greater temporal variability causes critical floods with less amount of total rainfall; and that 2) the greater imperviousness requires less rainfall to have critical floods. For validation, the proposed equation was applied for the flood warning system with two storm events occurred in 2010 and 2011 over 239 watersheds in Seoul. The results of the application showed high performance of the warning system in issuing the flood warning, with the hit, false and missed alarm rates at 68%, 32% and 7.4% respectively for the 2010 event and 49%, 51% and 10.7% for the event in 2011.

• Smart Media Journal
• /
• v.5 no.2
• /
• pp.42-50
• /
• 2016

In case of a emergency situation or a natural disaster, a warning notification system is an essential tool to notify at-risk people in advance and provide them useful information to survive the event. Although some systems have been proposed such as emergency alert system using android, SMS, or P2P overlay network, these works mainly focus on a reliable message distribution methods. In this paper, we proposed a novel design and implementation of a personalized warning notification system to help inform not only the at-risk people but also their family and friends about the coming disaster as well as escape plan and survival information. The system consists of three main modules: the user selection module, the knowledge based message generator, and message distribution modules. The user selection module collects the list of people involved in the event and sorts them based on their level of involvement (their location, working position and social relationships). The knowledge based message generator provides each person with a personalized message that is concise and contains only the necessary information for the particular person based on their working position and their involvement in the event. The message distribution module will then find a best path for sending the personalized messages based on trustiness of locations since network failures may exist in a disaster event. Additionally, the system also have a comprehensive database and an interactive web interface for both user and system administrator. For evaluation, the system was implemented and demonstrated successfully with a building on fire scenario.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.236-236
• /
• 2015

Flood is one of the major natural disasters affecting millions of people. Thailand also, frequently faces with this type of disaster. Especially, 2011 mega flood in Central Thailand, inundated highway severely attributed to the failure of national economic and risk to life. Lesson learned from such an extreme event caused flood monitoring and warning becomes one of the sound mitigations. The highway flood hazard mapping accomplished in this research is one of the strategies. This is due to highway flood is the potential risk to life and limb, and potential damage to property. Monitoring and warning therefore help reducing live and property losses. In this study, degree of highway flood hazard was assessed by weighting factors for each cause of the highway flood using Multi Criteria Analysis (MCA) based Analytic Hierarchy Process (AHP). These weighting factors are the essential information to classify the degree of highway flood hazard to enable pinpoint on flood monitoring and flood warning in hazard areas. The highway flood causes were then investigated. It was found that three major factors influence to the highway flood are namely the highway characteristics, the hydrological characteristics and the land topography characteristics. The weight of importance for each cause of the highway flood in the whole country was assessed by weighting 3 major factors influence to the highway flood. According to the result of MCA analysis, the highway, the hydrological and the land topography characteristics were respectively weighted as 35, 35 and 30 percent influence to the cause of highway flood. These weighting factors were further utilized to classify the degree of highway flood hazard. The Weight Linear Combination (WLC) method was used to compute the total score of all highways according to each factor. This score was later used to categorize highway flood as high, moderate and low degree of hazard levels. Highway flood hazard map accomplished in this research study is applicable to serve as the handy tool for highway flood warning. However, to complete the whole warning process, flood water level monitoring system for example the camera gauge should be installed in the hazard highway. This is expected to serve as a simple flood monitor as part of the warning system during such extreme or critical event.

• Convergence Security Journal
• /
• v.6 no.4
• /
• pp.67-73
• /
• 2006

The method which research a standardization from real time cyber threat is finding the suspicious indication above the attack against cyber space include internet worm, virus and hacking using analysis the event of each security system through correlation with the critical point, and draft a general standardization plan through statistical analysis of this evaluation result. It means that becomes the basis which constructs the effective cyber attack response system. Especially at the time of security accident occurrence, It overcomes the problem of existing security system through a definition of the event of security system and traffic volume and a concretize of database input method, and propose the standardization plan which is the cornerstone real time response and early warning system. a general standardization plan of this paper summarizes that put out of threat index, threat rating through adding this index and the package of early warning process, output a basis of cyber threat index calculation.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.6
• /
• pp.59-68
• /
• 2008

In this paper, we propose a method for selecting meaningful event packets from which can receive before anything else from seismograph according to allotted priority and estimate epicenter using selected packets. Event packets which received from each station will be evaluated with their onset time, signal period and SNR by statistical method and will be selected packets related with real earthquake's P-wave. And estimated epicenters using by 'Application of epicenter estimation using first P arrivals'. With local earthquakes occurred in 2007 were announced by KMA, collected event packets on earthquake happened date and selected p-wave related packets and estimated epicenter. After result of experiment, if an earthquake occurred within seismic networks, can estimate epicenter with small misfits just after event packets arrived from above 4 stations. Considering average distance of each station, in case of using all stations' data include other organization, can estimate and alert rapidly. It show this method is useful when construct a local earthquake early warning system later.

• Journal of Korea Water Resources Association
• /
• v.36 no.3 s.134
• /
• pp.375-384
• /
• 2003

Severe drought tends to occur in almost event five years in Korea. Drought responses have been well operated in close collaboration with the central, local government and the water management authorities on the institutional framework. However, the responses are usually post-activities to a drought event. The responses often face difficulties in operating and managing process due to an absence of a drought monitoring system and drought triggers. The objective of this study is to set up drought triggers through a time-spatial interpretation of drought index and the government responses during historical drought events. Drought triggers are divided into four categories: advisory, watch, warning and emergency stage. The range and drought-impacted area of an each stage in triggers have been addressed using drought index. Furthermore, a web-based drought monitoring system is illustrated.

• Journal of the Earthquake Engineering Society of Korea
• /
• v.25 no.2
• /
• pp.71-81
• /
• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.