Using social media data to construct and analyze knowledge graph for "7.20" Henan rainstorm flood event DOI Creative Commons

Haipeng Lu,

Shuliang Zhang,

Yu Gao

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: unknown, P. 105129 - 105129

Published: Dec. 1, 2024

Language: Английский

Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era DOI
Samuel Park,

Jaekyoung Kim,

Yejin Kim

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105496 - 105496

Published: May 5, 2024

Language: Английский

Citations

24
Assessment of Urban Flood Disaster Responses and Causal Analysis at Different Temporal Scales Based on Social Media Data and Machine Learning Algorithms DOI Creative Commons

Qichen Guo,

Sheng Jiao, Yuchen Yang

et al.

International Journal of Disaster Risk Reduction, Journal Year: 2025, Volume and Issue: 117, P. 105170 - 105170

Published: Jan. 5, 2025

Language: Английский

Citations

4
Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model DOI
Xiaotong Zhu, Hongwei Guo, Jinhui Jeanne Huang‬‬‬‬

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 108, P. 105508 - 105508

Published: May 5, 2024

Language: Английский

Citations

15
Assessing and interpreting perceived park accessibility, usability and attractiveness through texts and images from social media DOI
Xukai Zhao, Yuxing Lu, Wenwen Huang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 112, P. 105619 - 105619

Published: July 3, 2024

Language: Английский

Citations

9
Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui DOI Creative Commons

Songfeng Gao,

Tengfei Yang, Yang-Bin Xu

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(1), P. 465 - 465

Published: Jan. 6, 2025

Emergency situation awareness during sudden natural disasters presents significant challenges. Traditional methods, characterized by low spatial and temporal resolution as well coarse granularity, often fail to comprehensively capture disaster situations. However, social media platforms, a vital source of sensing, offer potential supplement situational awareness. This paper proposes an innovative framework for based on multimodal data from identify content related typhoon disasters. Integrating text image facilitates near real-time monitoring the public perspective. In this study, Typhoon Haikui (Strong No. 11 2023) was chosen case study validate effectiveness proposed method. We employed ERNIE language processing model complement Deeplab v3+ deep learning semantic segmentation extracting damage information media. A visualization analysis disaster-affected areas performed categorizing types. Additionally, Geodetector used investigate heterogeneity its underlying factors. approach allowed us analyze spatiotemporal patterns evolution, enabling rapid assessment facilitating emergency response efforts. The results show that method significantly enhances effectively identifying different types sensing data.

Language: Английский

Citations

1
Named entity recognition for construction documents based on fine-tuning of large language models with low-quality datasets DOI

Junyu Zhou,

Zhiliang Ma

Automation in Construction, Journal Year: 2025, Volume and Issue: 174, P. 106151 - 106151

Published: March 31, 2025

Language: Английский

Citations

1
Revealing public attitudes toward mobile cabin hospitals during Covid-19 pandemic: Sentiment and topic analyses using social media data in China DOI
Shenghua Zhou, Hongyu Wang, Dezhi Li

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 107, P. 105440 - 105440

Published: April 12, 2024

Language: Английский

Citations

8
An approach to exploring the spatial distribution and influencing factors of urban problems based on Land use types DOI

Jianling Jiao,

Yaxin Jin,

Ranran Yang

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 104, P. 105321 - 105321

Published: Feb. 29, 2024

Language: Английский

Citations

7
Investigating disaster response for resilient communities through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season DOI
Zihui Ma, Lingyao Li, Libby Hemphill

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 106, P. 105362 - 105362

Published: March 20, 2024

Language: Английский

Citations

7
State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review DOI Open Access

Jiayi Song,

Zhiyu Shao,

Ziyi Zhan

et al.

Water, Journal Year: 2024, Volume and Issue: 16(17), P. 2476 - 2476

Published: Aug. 30, 2024

In the context of increasing frequency urban flooding disasters caused by extreme weather, accurate and timely identification monitoring flood risks have become increasingly important. This article begins with a bibliometric analysis literature on identification, revealing that since 2017, this area has global research hotspot. Subsequently, it presents systematic review current mainstream technologies, drawing from both traditional emerging data sources, which are categorized into sensor-based (including contact non-contact sensors) big data-based social media surveillance camera data). By analyzing advantages disadvantages each technology their different focuses, paper points out largely emphasizes more “intelligent” technologies. However, these technologies still certain limitations, sensor techniques retain significant in practical applications. Therefore, future risk should focus integrating multiple fully leveraging strengths sources to achieve real-time flooding.

Language: Английский

Citations

5