Improving urban flood resilience via GDELT GKG analyses in China's Sponge Cities

Lü, Xiaohui; Chan, Faith Ka Shun; Li, Nan; Chen, Chuke; Chen, Wei‐Qiang; Chan, Hing Kai

doi:10.1038/s41598-022-24370-8

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Likewise, in China, climate change and sustainable urban development are extremely prominent issues (Zhai et al, 2019). Modern urban flood management includes engineering methods like infrastructure construction and public opinion mining to know more about their perception to ensure better public participation via social media and enhance preparation for flooding and response (Lu et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

Perception of sponge city for achieving circularity goal and hedge against climate change: a study on Weibo

Zeng

Zeng³

et al. 2023

IJCCSM

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Purpose Global climate change speeds up ice melting and increases flooding incidents. China launched a sponge city policy as a holistic nature-based solution combined with urban planning and development to address flooding due to climate change. Using Weibo analytics, this paper aims to study public perceptions of sponge city. Design/methodology/approach This study collected 53,586 sponge city contents from Sina Weibo via Python. Various artificial intelligence tools, such as CX Data Science of Simply Sentiment, KH Coder and Tableau, were applied in the study. Findings 76.8% of public opinion on sponge city were positive, confirming its positive contribution to flooding management and city branding. 17 out of 31 pilot sponge cities recorded the largest number of sponge cities related posts. Other cities with more Weibo posts suffered from rainwater and flooding hazards, such as Xi'an and Zhengzhou. Originality/value To the best of the authors’ knowledge, this study is the first to explore the public perception of sponge city in Sina Weibo.

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Section: Resultsmentioning

confidence: 99%

Perception of sponge city for achieving circularity goal and hedge against climate change: a study on Weibo

Zeng

Zeng³

et al. 2023

IJCCSM

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“…This trend suggests that economic development influences media coverage and public attention. The research by Lu et al (2022) also indicates that economically developed and urbanized cities receive more media attention. Additionally, media attention towards urban floods is often driven by the severity of the events.…”

Section: The Performance Of the Bert Modelmentioning

confidence: 99%

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

Fu,

Schultz,

Lyu

et al. 2024

Preprint

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Abstract. Urban floods present a threat in China, demanding an understanding of their spatiotemporal distribution. Current flood datasets primarily offer provincial-scale insights and lack temporal continuity, which leads to a challenge in detailed analysis. To create a consistent national dataset of flood events, this study introduces a machine learning framework by applying online news media as a primary data source to construct a county-level dataset of urban flood events from 2000 to 2022. Using the Bidirectional Encoder Representations from Transformers (BERT) model, we achieved robust performance in information extraction, with an F1 score of 0.86 and an exact match score of 0.82. Further, a combined model of Bidirectional Long Short-term Memory (BiLSTM) networks with a Conditional Random Field (CRF) layer effectively identified flood locations. Our analysis reveals that the temporal trend of flooded cities in the news-based dataset is similar to the China Flood and Drought Bulletin. Furthermore, the consistency of flood events in the news with the typhoon trajectory in two cases, and the connection between flood occurrences and flood conditioning factors, confirm the accuracy of spatial distribution. The validated news-based dataset analyzes urban floods in China from both temporal and spatial perspectives. First, this dataset shows the seasonal characteristics of flood events, which are concentrated in the summer. From 2000 to 2022, the peak year for floods was 2010, and excluding the influence of peak year, the overall temporal trend of total flood occurrence shows an increase. Spatially, the distribution of floods decreases from southeast to northwest, with Guangxi Province having the highest number of floods. Additionally, the Yangtze and Pearl River basins are most frequently affected by urban floods. The subtropical climate zone is the most susceptible to flooding. This study provides an automated and effective method for constructing a national flood event dataset and reveals the spatiotemporal characteristics of urban flooding in China.

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“…Rainfall was designed for return periods of 2, 5, 10, and 20 years referring to topography and design standards of pipe networks and pumping stations in Yi County. The rainfall intensities of a two-hour duration were calculated based on the Chicago design storm method [49], and the Baoding rainstorm formula Equation ( 4), which are shown in Figure 9. Rainfall was designed for return periods of 2, 5, 10, and 20 years referring to topography and design standards of pipe networks and pumping stations in Yi County.…”

Section: Relative Errormentioning

confidence: 99%

“…Overall, considering Yi County's land use and older buildings, the decision was made to use permeable pavements in residential and commercial areas, sunken greenbelts in green spaces, and rain barrels for roof rainwater collection. The results of LID parameters referring to related articles like [47][48][49][50] were shown in Table A3.…”

Section: Lid Implementationmentioning

confidence: 99%

A Framework Based on LIDs and Storage Pumping Stations for Urban Waterlogging

Li,

Luan,

Liu

et al. 2024

Remote Sensing

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Climate change has resulted in an increase in extreme rainstorm events, posing the challenges of urban waterlogging and runoff pollution. Low Impact Development (LID) is widely used to address the issues above, but its effectiveness is unknown in mountainous areas. Due to a flash flood and high flood peak, storage pumping stations are also needed to drain. Thus, a framework composed of storage pumping stations and Low Impact Developments (LIDs) was proposed based on the topography and the regional upstream and downstream relationships. The water quantity in this framework is applied to YI County in Hebei Province, China. The results showed that individual LIDs effectively reduced runoff volume, with the implementation area being more crucial than the location. Combining storage pumping stations with LIDs significantly reduces peak outflow and delays it by 5 to 51 min. The combined downstream implementation of storage pumping stations and LIDs yielded the most effective results. These findings offer important insights and management strategies for controlling waterlogging in mountainous cities of developing countries.

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Improving urban flood resilience via GDELT GKG analyses in China's Sponge Cities

Cited by 11 publications

References 23 publications

Perception of sponge city for achieving circularity goal and hedge against climate change: a study on Weibo

Perception of sponge city for achieving circularity goal and hedge against climate change: a study on Weibo

Extracting Spatiotemporal Flood Information from News Texts Using Machine Learning for a National Dataset in China

A Framework Based on LIDs and Storage Pumping Stations for Urban Waterlogging

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