• Journal of Internet Computing and Services
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• v.18 no.5
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• pp.69-77
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• 2017

In this paper, a disaster response scheme based on deep learning and augmented reality technology is proposed and a network resource reservation scheme is presented accordingly. The features of deep learning, augmented reality technology and its relevance to the disaster areas are explained. Deep learning technology can be used to accurately recognize disaster situations and to implement related disaster information as augmented reality, and to enhance disaster response capabilities by providing disaster response On-site disaster response agent, ICS (Incident Command System) and MCS (Multi-agency Coordination Systems). In the case of various disasters, the fire situation is focused on and it is proposed that a plan to strengthen disaster response capability effectively by providing fire situation recognition based on deep learning and augmented reality information. Finally, a scheme to secure network resources to utilize the disaster response method of this paper is proposed.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.145-145
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• 2022

Availability of abundant water resources data in developing countries is a great concern that has hindered the adoption of deep learning techniques (DL) for disaster prevention and mitigation. On the contrary, over the last two decades, a sizeable amount of DL publication in disaster management emanated from developed countries with efficient data management systems. To understand the current state of DL adoption for solving water-related disaster management in developing countries, an extensive bibliometric review coupled with a theory-based analysis of related research documents is conducted from 2003 - 2022 using Web of Science, Scopus, VOSviewer software and PRISMA model. Results show that four major disasters - pluvial / fluvial flooding, land subsidence, drought and snow avalanche are the most prevalent. Also, recurrent flash floods and landslides caused by irregular rainfall pattern, abundant freshwater and mountainous terrains made India the only developing country with an impressive DL adoption rate of 50% publication count, thereby setting the pace for other developing countries. Further analysis indicates that economically-disadvantaged countries will experience a delay in DL implementation based on their Human Development Index (HDI) because DL implementation is capital-intensive. COVID-19 among other factors is identified as a driver of DL. Although, the Long Short Term Model (LSTM) model is the most frequently used, but optimal model performance is not limited to a certain model. Each DL model performs based on defined modelling objectives. Furthermore, effect of input data size shows no clear relationship with model performance while final model deployment in solving disaster problems in real-life scenarios is lacking. Therefore, data augmentation and transfer learning are recommended to solve data management problems. Intensive research, training, innovation, deployment using cheap web-based servers, APIs and nature-based solutions are encouraged to enhance disaster preparedness.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.582-585
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• 2011

During disaster response, prioritization of limited resources is one of the most important bust challenging tasks. At the same time, it is imperative to timely provide the rescuers with the adequate equipment to facilitate lifesaving operations. However, supply of high demand equipment was insufficient during the initial phase of disaster response, challenging lifesaving operations in the case of the 9-11 terrorist attacks. In respond to the Haiti Earthquake, spatial information of the geographic area was not sufficient to support the search and rescue operations in the early phase of disaster response. However, with the help of civilians, information such as road names, infrastructure damage, and victim locations were updated into the spatial data repository. At the same time, resource outside of the disaster affected zone converges into the area to assist the response efforts, which is the effect of convergence that often made resource coordination challenging in large scale disasters. To efficiently collect information and utilize the converging resources, this paper proposes a flexible data repository for information update for equipment utilization in large scale disaster response scenarios.

• Journal of the Society of Disaster Information
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• v.7 no.2
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• pp.140-150
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• 2011

Life Environment is rapidly changing and large scale disasters are increasing from the global warming. Although the disaster repair resources are deployed to the disaster fields, the prevention of the disasters is the most effective countermeasures. the disaster sign data is based on the rule of Heinrich. Automatic extraction of the disaster sign data from the web is the focused issues in this paper. We defined the automatic extraction processes and applied information, such as accident nouns, disaster filtering nouns, disaster sign nouns and rules. Using the processes, we implemented the disaster sign data management system. In the future, the applied information must be continuously updated, because the information is only the extracted and analytic result from the some disaster data.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.111-123
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• 2004

Recently, though damage caused by intensive rainfall and typhoon happens frequently, we could not forecast or predict a disaster, due to the difficulty of obtaining exact information about it. For efficient disaster management, the most urgent need is the preparation of a flood forecast-warning system. Therefore, we need to provide a program that has the ability of inundation analysis and flood forecast-warning using a geographic information system, and using domestic technology rather than that from foreign countries. In this research, we constructed a FDMS(Flood Disaster Management System) that is able to analyze real-time inundation data, and usins the GIS(Ceographic Information System) with prompt analyzing of hydrologic-topographical parameters and runoff-computation. Moreover, by expressing inundation analysis in three-dimensions, we were able to get to the inundation area with ease. Finally, we expect that the application of this method in the (food forecast-warning system will have great role in reducing casualties and damage.

• Water Engineering Research
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• v.4 no.4
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• pp.187-192
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• 2003

The rainfall observation systems have largely been improved in Japan. The Japan Meteorological Agency, prefecture governments, and other administrative bodies have also increased the number of rain gauges thru out the country. The density of observatories is now one per several $\km^2$. Heavy rainfall information systems have been improved. Besides it, the Internet was popularized in the late 1990s, and has been used to transmit data of heavy rainfall. Internet accessible cellular phones have been popular in Japan since 1999. Such phones are expected to be useful in the field of disaster warning announcements, because they can automatically notify users bye-mail of pending disasters. The use of the Internet during natural disasters is groundbreaking in Japan today. However, in order to use disaster information effectively on Internet it is necessary to investigate how to use the information during the rainfall disaster. Therefore in our study we suggest methods on the effective construction and their use of information technology on Internet.

• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.227-239
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• 2015

To decrease the damage of infrastructure from the natural disaster, initial reaction and emergent recovery are important. But up to now emergent recovery has mainly focused on the situation notices owing to the lack of technical support. In this paper, we described a web-based support system for disaster damage recovery of facility and electronic documents for requesting support of recovery resources. Recovery methods are decided using the damage recovery scenarios based on the acquired damage information, and the allocated recovery resources information is delivered to the recovery resources owner using the electronic documents. This system could readily be used for disaster response and emergency recovery.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.1100-1101
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• 2005

• Journal of the Korea Society of Computer and Information
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• v.21 no.2
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• pp.43-49
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• 2016

In this paper, we propose the controllability function and service design to efficiently implement the control of the disaster communication network, using Internet of Things(IoT) Technology. In accordance with the spread of Information Communication Technology(ICT), the era for building a disaster communications system without exclusion over the entire areas has emerged. National wireless mesh networks for public safety and disaster relief have been evolving to strengthen the pre-disaster response system using the latest technologies through the convergence of various technologies and services from the viewpoint of the command and control between disaster response agencies. In line with such a technological paradigm shift, the controllability of the objects in the IoT has been emerging as a key quality requirement of a disaster communications system. In this study, the objects are classified by the subject of control according to the IoT component, such as data, network resources and services in order to effectively implement their controllability. In addition, based on the destination of this controllability, technologies and services have been designed that can reduce the damage caused by disasters. Technologies and services that were derived from this study must be implemented in the current disaster safety network systems together with the establishment of an infrastructure for the networks in order that all persons are able to effectively utilize the disaster communications system for their safety.

• 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.