首页|National Research Institute of Astronomy and Geophysics Researcher Reveals New F indings on Machine Learning (Employing Machine Learning for Seismic Intensity Es timation Using a Single Station for Earthquake Early Warning)
National Research Institute of Astronomy and Geophysics Researcher Reveals New F indings on Machine Learning (Employing Machine Learning for Seismic Intensity Es timation Using a Single Station for Earthquake Early Warning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting out of Helwan, Egypt, by New sRx editors, research stated, “An earthquake early-warning system (EEWS) is an i ndispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively mana ging earthquake disasters and implementing successful risk-reduction strategies. ” Our news journalists obtained a quote from the research from National Research I nstitute of Astronomy and Geophysics: “In this regard, the utilization of an Int ernet of Things (IoT) network enables the realtime transmission of on-site inte nsity measurements. This paper introduces a novel approach based on machine-lear ning (ML) techniques to accurately and promptly determine earthquake intensity b y analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INS N) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achi eving an impressive accuracy rate of 99.05% in forecasting based o n any single component from the 3C.”
National Research Institute of Astronomy and GeophysicsHelwanEgyptCyborgsEmerging TechnologiesMachine Learni ng
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