Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105496 - 105496

Опубликована: Май 5, 2024

Язык: Английский

---

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

International Journal of Disaster Risk Reduction, Год журнала: 2025, Номер 117, С. 105170 - 105170

Опубликована: Янв. 5, 2025

Язык: Английский

---

Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105508 - 105508

Опубликована: Май 5, 2024

Язык: Английский

---

Assessing and interpreting perceived park accessibility, usability and attractiveness through texts and images from social media

Sustainable Cities and Society, Год журнала: 2024, Номер 112, С. 105619 - 105619

Опубликована: Июль 3, 2024

Язык: Английский

---

Enhancing Disaster Situation Awareness Through Multimodal Social Media Data: Evidence from Typhoon Haikui

Applied Sciences, Год журнала: 2025, Номер 15(1), С. 465 - 465

Опубликована: Янв. 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.

Язык: Английский

---

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

Automation in Construction, Год журнала: 2025, Номер 174, С. 106151 - 106151

Опубликована: Март 31, 2025

Язык: Английский

---

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

Sustainable Cities and Society, Год журнала: 2024, Номер 106, С. 105362 - 105362

Опубликована: Март 20, 2024

Язык: Английский

---

Revealing public attitudes toward mobile cabin hospitals during Covid-19 pandemic: Sentiment and topic analyses using social media data in China

Sustainable Cities and Society, Год журнала: 2024, Номер 107, С. 105440 - 105440

Опубликована: Апрель 12, 2024

Язык: Английский

---

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

Sustainable Cities and Society, Год журнала: 2024, Номер 104, С. 105321 - 105321

Опубликована: Фев. 29, 2024

Язык: Английский

---

State-of-the-Art Techniques for Real-Time Monitoring of Urban Flooding: A Review

Water, Год журнала: 2024, Номер 16(17), С. 2476 - 2476

Опубликована: Авг. 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.

Язык: Английский

---