Rapid assessment of seismic intensity based on Sina Weibo — A case study of the changning earthquake in Sichuan Province, China

Yao, Kezhen; Yang, Saini; Tang, Jiting

doi:10.1016/j.ijdrr.2021.102217

Cited by 12 publications

(5 citation statements)

References 19 publications

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“…The acquisition and assessment of disaster information in China relies mainly on disaster-related government departments at all levels. As disaster emergency management faces new challenges, gaps remain in the timeliness and completeness of rapid disaster assessments [15].…”

Section: Discussionmentioning

confidence: 99%

A smart emergency platform for earthquake disasters based on multisource monitoring data: a case study of Lanzhou City

Du,

Li,

Xie

2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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Earthquakes are one of the natural disasters with the greatest threat to human life, and since earthquake forecasting is not accurate, one of the important tasks of post-earthquake emergency response is to assess the earthquake damage in time and guide the decision-making of emergency rescue and relief materials distribution. The 3D seismic information sharing platform provided by Cesium indeed provides an effective application for 3D visualization of seismic information in the existing network. In this paper, Vue and Cesium jointly build the front end of the platform, Java programming language, and Spring Boot framework to build the back end, using MySQL as the database and MyBatis-plus ORM framework to achieve data persistence. At the same time, Redis is used to store cached data to improve read and write speed and performance. The platform can capture real-time earthquake information and perform intensity estimation based on elliptic attenuation model equations after the earthquake, use empirical models to assess economic losses and casualties, and obtain the Gaode API for route planning to rescue points. The platform not only helps the government to carry out post-earthquake activities but also allows the public to understand the effects of earthquakes more clearly.

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Section: Discussionmentioning

confidence: 99%

A smart emergency platform for earthquake disasters based on multisource monitoring data: a case study of Lanzhou City

Du,

Li,

Xie

2024

Second International Conference on Environmental Remote Sensing and Geographic Information Technology (ERSGIT 2023)

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“…Weibo text data provided by the Chinese social media network platform Sina were used mainly for the extraction of disaster locations [48]. Considering the time span of the 7.20 extreme rainstorm in Zhengzhou, this study used Python web crawler technology to obtain a total of 22,860 texts from Zhengzhou City from 19 to 23 July 2021, taking "Zhengzhou rainstorm" as the keyword, with each Weibo record containing several fields, such as user ID, posting time, posting content, posting location, and posting tool.…”

Section: Data Sources and Preprocessingmentioning

confidence: 99%

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

Zhang,

Jia,

Liu

et al. 2023

Remote Sensing

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The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

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“…Furthermore, deep-learning-based real-time seismic intensity prediction has emerged as a current research focus (Otake et al, 2020;Chen et al, 2022). Internet data, remote sensing and radar data are widely utilized in earthquake damage assessment (Dell'acqua and Gamba, 2012;Hao et al, 2012;Xu et al, 2013;Yao et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

Zhao,

Chen,

Zhang

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Accurate and rapid assessment of seismic intensity after a destructive earthquake is essential for efficient early emergency response. We proposed an improved method, AL-SM99, to assess seismic intensity by analyzing aftershock sequences that occur within 2 h of mainshocks. The implementation effect and application conditions of this method were illustrated using 27 earthquakes with Mw 6.5–8.3 that occurred globally between 2000 and 2023. When the fault system in the seismic region is clear and simple, the robust LOWESS-fitted (locally weighted regression program) curves could be used to estimate the location and length of the fault rupture. LOWESS results can indicate the overall rupture trend and make reliable rupture-scale judgments even when the fault system is complex. When Mw ≥ 7.0 and the number of aftershocks exceeds 40, the AL-SM99 intensity evaluation results may be more reliable. Using aftershock catalogues obtained by conventional means allows for a stable assessment of seismic intensities within 1.5 h of the mainshock. When the number of aftershocks is sufficiently large, the intensity assessment time can be greatly reduced. With early accessible aftershocks, we can quickly determine the rupture fault planes and have a better estimate of the seismic intensities. The results of the intensity assessment provide a useful guide for determining the extent of the hardest-hit areas. By expanding the data sources for seismic intensity assessment, the early accessible data are utilized adequately. This study provides a valuable reference point for investigating the relationship between early aftershock events and fault rupture.

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Rapid assessment of seismic intensity based on Sina Weibo — A case study of the changning earthquake in Sichuan Province, China

Cited by 12 publications

References 19 publications

A smart emergency platform for earthquake disasters based on multisource monitoring data: a case study of Lanzhou City

A smart emergency platform for earthquake disasters based on multisource monitoring data: a case study of Lanzhou City

Spatiotemporal Information Mining for Emergency Response of Urban Flood Based on Social Media and Remote Sensing Data

Rapid estimation of seismic intensities by analyzing early aftershock sequences using the robust locally weighted regression program (LOWESS)

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