CrowdQuake+: Data-driven Earthquake Early Warning via IoT and Deep Learning

Wu, Aming; Lee, Jang-Soo; Khan, Irshad; Kwon, Youngwoo

doi:10.1109/bigdata52589.2021.9671971

Cited by 10 publications

(6 citation statements)

References 18 publications

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“…Creation of warning maps (Cao et al, 2017) and in combination with machine learning can help to predict fire spread (Huot et al, 2022) Earthquakes Improvement of aftershock forecasts and earthquake early warning (Wu et al, 2021) The use of mobile phones (Zambrano et al, 2017;Wu et al, 2021) can help monitor earthquakes (Taale et al, 2021), and in combination with machine learning improve earthquake early warning.…”

Section: Wildfiresmentioning

confidence: 99%

“…Costache and Tien Bui, 2019;Arabameri et al, 2020). Further, Mousavi and Beroza (2022) have shown that machine learning, deep learning, and AI applications are already broadly used in seismology and have the potential to significantly influence the field: i) using deep learning for earthquake early warning (EEW; Wu et al, 2021); ii) detecting seismic signals and forecasting seismic activities with machine learning (Seydoux et al, 2020); and, to a lesser extent and even controversially, iii) helping to predict earthquakes (e.g. Banna et al, 2020;Marhain et al, 2021).…”

Section: Wildfiresmentioning

confidence: 99%

“…This is especially promising because it seems to be cheaper than previous procedures used for modelling (Essam et al, 2021). AI can help enhance EEW (Meier et al, 2020;Iaccarino et al, 2021;Wu et al, 2021;Datta et al, 2022) and improve methods to forecast earthquakes (Mousavi and Beroza, 2023;Beroza et al, 2021) using e.g. deep learning (Seydoux et al, 2020).…”

Section: First Survey Roundmentioning

confidence: 99%

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What does my technology facilitate`? A toolbox to help researchers understand the societal impact of a technology in the context of disasters

Kuratle,

Dallo,

Marti

et al. 2024

Seismica

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Disaster risk is increasing globally. Emerging technologies – Artificial Intelligence, Internet of Things, and remote sensing – are becoming more important in supporting disaster risk reduction and enhancing safety culture. Despite their presumed benefits, most research focuses on their technological potential, whereas societal aspects are rarely reflected. Taking a societal perspective is vital to ensure that these technologies are developed and operated in ways that benefit societies’ resilience, comply with ethical standards, are inclusive, and address potential risks and challenges. Therefore, we were particularly interested in understanding how societal impacts can be considered and leveraged throughout the development process. Based on an explorative literature review, we developed a toolbox for professionals working on emerging technologies in disaster risk reduction. By applying a Delphi study with experts on AI in seismology, we iteratively adapted and tested the toolbox. The results show that there is a need for guided reflection in order to foster discussion on the societal impacts. They further indicate a gap in the common understanding that is crucial for developing inclusive technologies or defining regulations. Our toolbox was found to be useful for professionals in reflecting on their developments and making technologies societally relevant, thereby enhancing societies’ resilience.

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Section: Wildfiresmentioning

confidence: 99%

Section: Wildfiresmentioning

confidence: 99%

Section: First Survey Roundmentioning

confidence: 99%

See 1 more Smart Citation

What does my technology facilitate`? A toolbox to help researchers understand the societal impact of a technology in the context of disasters

Kuratle,

Dallo,

Marti

et al. 2024

Seismica

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“…CrowdQuake+ [11] is an extension of the CrowdQuake network: While the original network leverages only on smartphones, CrowdQuake+ is able to process data from Internet-of-Things sensors. However, the overall architecture is the same, as well as the limitations discussed in Section 3.…”

Section: Related Workmentioning

confidence: 99%

Earthquake Detection at the Edge: IoT Crowdsensing Network

Bassetti

Panizzi

2022

Information

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State-of-the-art Earthquake Early Warning systems rely on a network of sensors connected to a fusion center in a client–server paradigm. The fusion center runs different algorithms on the whole data set to detect earthquakes. Instead, we propose moving computation to the edge, with detector nodes that probe the environment and process information from nearby probes to detect earthquakes locally. Our approach tolerates multiple node faults and partial network disruption and keeps all data locally, enhancing privacy. This paper describes our proposal’s rationale and explains its architecture. We then present an implementation that uses Raspberry, NodeMCU, and the Crowdquake machine learning model.

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“…LCIS-based network system has some notable new features from other MEMS instruments; 1) it uses an LTE wireless communication network and does not require a separate GPS sensor, 2) the system, called "CrowdQuake," has been newly established to process the large amount of real-time data [24], 3) it is scalable thanks to implementing SKT's infrastructure resources.…”

Section: Introductionmentioning

confidence: 99%

Development of Seismic Intensity Maps Using Low-Cost Micro-Electro-Mechanical Systems Seismic Network

et al. 2023

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The Korea Meteorological Administration (KMA), SK Telecom, and Kyungpook National University collaborated to develop the low-cost IoT seismometer (LCIS) that embeds a micro-electromechanical systems accelerometer and LTE communication sensor. Currently, approximately 7,000 stations (the average density is 0.07/km 2 ) with LCIS devices are operating in real-time on the Korean Peninsula, which is about 25 times higher density than the national seismic network with high performance seismometers operated by KMA. The majority of LCISs are installed inside buildings so that adjustment is applied to convert to the free-surface equivalent vibration. Researchers derived seismic intensity maps using only LCIS network data, which showed very similar distributions to the maps from the high-performance network. This validates the usefulness of the LCIS network for Modified Mercalli intensity (MMI) map generation and earthquake early warning system.

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CrowdQuake+: Data-driven Earthquake Early Warning via IoT and Deep Learning

Cited by 10 publications

References 18 publications

What does my technology facilitate`? A toolbox to help researchers understand the societal impact of a technology in the context of disasters

What does my technology facilitate`? A toolbox to help researchers understand the societal impact of a technology in the context of disasters

Earthquake Detection at the Edge: IoT Crowdsensing Network

Development of Seismic Intensity Maps Using Low-Cost Micro-Electro-Mechanical Systems Seismic Network

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