Articles | Volume 20, issue 4
https://doi.org/10.5194/nhess-20-921-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-20-921-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Apr 2020
Research article |
| 03 Apr 2020
| 03 Apr 2020
Design and implementation of a mobile device app for network-based earthquake early warning systems (EEWSs): application to the PRESTo EEWS in southern Italy
Simona Colombelli
CORRESPONDING AUTHOR
Department of Physics “Ettore Pancini”, University of Naples
Federico II, Naples, Italy
Francesco Carotenuto
Department of Physics “Ettore Pancini”, University of Naples
Federico II, Naples, Italy
Luca Elia
Department of Physics “Ettore Pancini”, University of Naples
Federico II, Naples, Italy
Aldo Zollo
Department of Physics “Ettore Pancini”, University of Naples
Federico II, Naples, Italy
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- Actionable and understandable? Evidence-based recommendations for the design of (multi-)hazard warning messages I. Dallo et al. 10.1016/j.ijdrr.2022.102917
- Earthquake Event Recognition on Smartphones Based on Neural Network Models M. Chen et al. 10.3390/s22228769
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- Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System M. Abdalzaher et al. 10.1109/TGRS.2024.3492023
- Applications and challenges of artificial intelligence in the field of disaster prevention, reduction, and relief C. Xu & Z. Xue 10.1016/j.nhres.2023.11.011
- Chinese Nationwide Earthquake Early Warning System and Its Performance in the 2022 Lushan M6.1 Earthquake C. Peng et al. 10.3390/rs14174269
- Peak ground acceleration prediction for on-site earthquake early warning with deep learning Y. Liu et al. 10.1038/s41598-024-56004-6
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- Threshold-based earthquake early warning for high-speed railways using deep learning J. Zhu et al. 10.1016/j.ress.2024.110268
- Developing ground motion prediction models for West Java: A machine learning approach to support Indonesia's earthquake early warning system A. Rachmadan et al. 10.1016/j.acags.2024.100212
- MEANet: Magnitude estimation via physics-based features time series, an attention mechanism, and neural networks J. Song et al. 10.1190/geo2022-0196.1
- Design of vibration sensors based on fibre Bragg grating type composites for earthquake detection and early warning application B. Xia et al. 10.1166/mex.2024.2662
- Spatial digital twin framework for overheight vehicle warning and re-routing system O. Trembearth et al. 10.1007/s44212-024-00054-8
30 citations as recorded by crossref.
- Support Vector Machine-Based Rapid Magnitude Estimation Using Transfer Learning for the Sichuan–Yunnan Region, China J. Zhu et al. 10.1785/0120210232
- Hybrid Deep-Learning Network for Rapid On-Site Peak Ground Velocity Prediction J. Zhu et al. 10.1109/TGRS.2022.3230829
- Design and implementation of earthquake early warning dissemination mobile app for Uttarakhand (India) G. Rathore et al. 10.1007/s10950-022-10124-6
- Real-Time Seismic Intensity Measurements Prediction for Earthquake Early Warning: A Systematic Literature Review Z. Cheng et al. 10.3390/s23115052
- Machine Learning-Based Rapid Epicentral Distance Estimation from a Single Station J. Zhu et al. 10.1785/0120230267
- Earthquake Early Warning (EEW) System: System Architecture, Data Modelling, and User Interface Design H. Noprisson 10.32628/CSEIT2173136
- On-site instrumental seismic intensity prediction for China via recurrent neural network and transfer learning J. Zhu et al. 10.1016/j.jseaes.2023.105610
- An Evolutionary Gravitational Neocognitron Neural Network for Earthquake Parameters Observation in IoT System-Based Earthquake Early Warning K. Manikannan et al. 10.1142/S1793431124500167
- A Communication, Management and Tracking Mobile Application for Enhancing Earthquake Preparedness and Situational Awareness in the Event of an Earthquake P. Kirci et al. 10.3390/su15020970
- Multilevel Assessment of Seismic Damage and Vulnerability of Masonry Buildings (MUSE-DV) in Historical Centers: Development of a Mobile Android Application L. Sbrogiò et al. 10.3390/su14127145
- Earthquake Prediction and Alert System Using IoT Infrastructure and Cloud-Based Environmental Data Analysis C. Rosca & A. Stancu 10.3390/app142210169
- Early detection of earthquake magnitude based on stacked ensemble model A. Joshi et al. 10.1016/j.jaesx.2022.100122
- A user experience evaluation of a mobile application for disseminating site‐specific impact‐based flood warnings: The A4alerts app E. Meléndez‐Landaverde & D. Sempere‐Torres 10.1111/jfr3.12951
- Design and implementation of Internet-of-Things software monitoring and early warning system based on nonlinear technology H. Ma et al. 10.1515/nleng-2022-0036
- Magnitude Estimation for Earthquake Early Warning Using a Deep Convolutional Neural Network J. Zhu et al. 10.3389/feart.2021.653226
- Magnitude Estimation for Earthquake Early Warning with Multiple Parameter Inputs and a Support Vector Machine J. Zhu et al. 10.1785/0220210144
- Actionable and understandable? Evidence-based recommendations for the design of (multi-)hazard warning messages I. Dallo et al. 10.1016/j.ijdrr.2022.102917
- Earthquake Event Recognition on Smartphones Based on Neural Network Models M. Chen et al. 10.3390/s22228769
- Magnitude estimation and ground motion prediction to harness fiber optic distributed acoustic sensing for earthquake early warning I. Lior et al. 10.1038/s41598-023-27444-3
- Research on Interference Signal Recognition in P Wave Pickup and Magnitude Estimation D. Yin et al. 10.1007/s10706-023-02648-6
- Using Deep Learning for Rapid Earthquake Parameter Estimation in Single-Station Single-Component Earthquake Early Warning System M. Abdalzaher et al. 10.1109/TGRS.2024.3492023
- Applications and challenges of artificial intelligence in the field of disaster prevention, reduction, and relief C. Xu & Z. Xue 10.1016/j.nhres.2023.11.011
- Chinese Nationwide Earthquake Early Warning System and Its Performance in the 2022 Lushan M6.1 Earthquake C. Peng et al. 10.3390/rs14174269
- Peak ground acceleration prediction for on-site earthquake early warning with deep learning Y. Liu et al. 10.1038/s41598-024-56004-6
- DEPREM SEVİYE SINIFLANDIRMASI İÇİN HİBRİT BİR CONVLSTM MODELİ: KARŞILAŞTIRMALI BİR ANALİZ A. Utku 10.17780/ksujes.1467269
- Threshold-based earthquake early warning for high-speed railways using deep learning J. Zhu et al. 10.1016/j.ress.2024.110268
- Developing ground motion prediction models for West Java: A machine learning approach to support Indonesia's earthquake early warning system A. Rachmadan et al. 10.1016/j.acags.2024.100212
- MEANet: Magnitude estimation via physics-based features time series, an attention mechanism, and neural networks J. Song et al. 10.1190/geo2022-0196.1
- Design of vibration sensors based on fibre Bragg grating type composites for earthquake detection and early warning application B. Xia et al. 10.1166/mex.2024.2662
- Spatial digital twin framework for overheight vehicle warning and re-routing system O. Trembearth et al. 10.1007/s44212-024-00054-8
Latest update: 23 Apr 2025
Short summary
We developed a mobile app for Android devices which receives the alerts generated by a network-based early warning system, predicts the expected ground-shaking intensity and the available lead time at the user position, and provides customized messages to inform the user about the proper reaction to the alert. The app represents a powerful tool for informing in real time a wide audience of end users and stakeholders about the potential damaging shaking in the occurrence of an earthquake.
We developed a mobile app for Android devices which receives the alerts generated by a...
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