Location correction technique based on mobile communication base station for earthquake population heat map

Liu, Xiaoli; Li, Xue; Chen, Xiaolin; Liu, Zhumei; Li, Shengle

doi:10.1016/j.geog.2018.01.003

Cited by 8 publications

(8 citation statements)

References 3 publications

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“…It was used for matching geographic population distribution in earthquake areas [70]. They found that the proposed 'heat map' was efficiently produced for locating the affected population [70]. This research found that an important point that IT could enhance efficiency and accuracy in disaster response phase.…”

Section: Perceived Usefulness (Pu)mentioning

confidence: 94%

“…In this study, using of mobile phones for DRM refers to communication on mobile devices that are spontaneity without accessing websites or mobile applications. Communication on mobile phone was used for detecting population in disaster zone and location [41,70]. It can be used to monitor water levels and send emergency alerts to authorities and population [71].…”

Section: Mobile Phonesmentioning

confidence: 99%

“…An additional case of mobile phone is with the location feature. It was used for matching geographic population distribution in earthquake areas [70]. They found that the proposed 'heat map' was efficiently produced for locating the affected population [70].…”

Section: Perceived Usefulness (Pu)mentioning

confidence: 99%

See 2 more Smart Citations

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

Meechang¹,

Leelawat²,

Tang³

et al. 2020

EngJ

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The numbers of natural disaster events are continuously affecting human and the world economics. For coping with disaster, several sectors try to develop the frameworks, systems, technologies and so on. However, there are little researches focusing on the usage behavior of Information Technology (IT) for disaster risk management (DRM). Therefore, this study investigates the affecting factors on the intention to use IT for mitigating disaster's impacts. This study conducted a systematic review with the academic researches during 2011-2018. Two important factors from the Technology Acceptance Model (TAM) and others are used in describing individual behavior. In order to investigate the potential factors, the technology platforms are divided into nine types. According to the findings, computer software such as GIS applications are frequently used for simulation and spatial data analysis. Social media is preferred among the first choices during disaster events in order to communicate about situations and damages. Finally, we found five major potential factors which are Perceived Usefulness (PU), Perceived Ease of Use (PEOU), information accessibility, social influence, and disaster knowledge. Among them, the most essential one of using IT for disaster management is PU, while PEOU and information accessibility are more important in the web platforms.

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Section: Perceived Usefulness (Pu)mentioning

confidence: 94%

Section: Mobile Phonesmentioning

confidence: 99%

See 1 more Smart Citation

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

Meechang¹,

Leelawat²,

Tang³

et al. 2020

EngJ

View full text Add to dashboard Cite

show abstract

“…They demonstrated very promising results regarding the prediction of earthquake magnitudes in the state of California. In [28], the authors proposed a real-time collection and classification algorithm of mobile phone position data by stream processing environments such as Kafka and Spark to produce a high precision heat map of the population affected by the earthquake. An integrated disaster management system developed through the combination of Hadoop and Spark was presented in [29].…”

Section: A Bda For Disaster Managementmentioning

confidence: 99%

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

et al. 2019

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The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems lack the support for multiple new data sources and real-time big data processing tools that can assist decision makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA-and IoT-based disaster management environments. The reference model with its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research. INDEX TERMS Big data analytics, data sources, disaster communications, disaster management, Internet of Things, reference model, taxonomy.

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“…With the development of communication technologies from 2G to 5G, the capacity of single base station access has increased. However, to avoid a dynamic crush from a flash crowd, automatically providing the appropriate response time to meet the user expectations and supporting an uninterrupted use of communication services remain challenging problems [1], [2].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Event Detection Method for Massive Wireless Access Prediction

et al. 2019

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The scale of mobile users for parallel access is constrained by the capacity of the base stations. When extremely dense terminal access exceeds the capacity of the base stations, access failure and a performance degradation will occur. The early detection and prediction of important events and the timely detection of possible large-scale terminal access are significant aspects in ensuring the quality of the communication achieved. For the automatic detection of events, methods based on a neural network can learn features automatically without feature engineering and have been proven to be efficient for event detection. As is well known, constructing an adequate input vector that can represent sufficient information is a challenge to a neural network-based approach, particularly for problems caused by Chinese word segmentation and too many unknown communication words. To cope with this problem, a novel representation method that combines the different features with word vectors is proposed to deal with the problem of Chinese event trigger identification. We then use a gated recurrent unit network to train and predict the event trigger and carry out comparative experiments on different methods and feature combinations. The experiment results of the proposed model show that the F1 value can reach 84% for the experimental dataset. Furthermore, the combination of lexical and syntactic features with a neural network was proven to be helpful for this task, although the contributions vary in magnitude for different features.Our study provides directions for further research on the use of lexical and syntactic features with a neural network for an event detection task.

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Location correction technique based on mobile communication base station for earthquake population heat map

Cited by 8 publications

References 3 publications

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The Acceptance of Using Information Technology for Disaster Risk Management: A Systematic Review

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

An Automatic Event Detection Method for Massive Wireless Access Prediction

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