Dynamic risk assessment of compound hazards  based on VFS–IEM–IDM: a case study of  typhoon–rainstorm hazards in Shenzhen, China

Gong, Wenwu; Jiang, Jie; Yang, Lili

doi:10.5194/nhess-22-3271-2022

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The annual average rainfall is 1935.8 mm, and the time distribution shows that the rainfall is mainly concentrated in April to September, with a spatial trend of decreasing rainfall from the southeast to the northwest. Typhoons and rainstorms are the most frequently occurring hazards in Shenzhen (Gong et al, 2022). Shenzhen is prone to frequent short-duration rainstorms, which often result in severe waterlogging in the city and, sometimes, can even cause casualties .…”

Section: Statistical Featuresmentioning

confidence: 99%

Nature-Based Solutions for Urban Water Management

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Statistical Featuresmentioning

confidence: 99%

Nature-Based Solutions for Urban Water Management

2024

Frontiers Research Topics

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Section: Study Areamentioning

confidence: 99%

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

Zhang

Jian

Chen

et al. 2023

Front. Environ. Sci.

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In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.

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“…Numerous flood-prone low-lying sites have been incorporated into building plans, and poor drainage capacity has further exacerbated the risk of flooding [3]. During extreme rainstorms or typhoons, the intensity of the storm often far exceeds the design drainage capacity of the municipal system [4]. The surface rainfall cannot be drained in a timely manner, thus forming surface runoff and further converging into waterlogging.…”

Section: Introductionmentioning

confidence: 99%

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

Zhang

Zeng

Huang

et al. 2023

IJERPH

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The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.

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Dynamic risk assessment of compound hazards based on VFS–IEM–IDM: a case study of typhoon–rainstorm hazards in Shenzhen, China

Cited by 6 publications

References 28 publications

Nature-Based Solutions for Urban Water Management

Nature-Based Solutions for Urban Water Management

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

Multi-Source Data Fusion and Hydrodynamics for Urban Waterlogging Risk Identification

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