A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier

Tang, Xianzhe; Shu, Yuqin; Lian, Yanqing; Zhao, Yaolong; Fu, Yingchun

doi:10.1016/j.scitotenv.2018.02.172

Cited by 95 publications

(61 citation statements)

References 28 publications

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“…The stronger influence of impervious surface percentage on waterlogging frequency was located in the Dong Da, Thanh Xuan and Hoang Mai Districts (Figure 9e). This result agrees with a number of studies [12,26] which concluded that an increase in the impervious surface area may be one of the most common causes of urban waterlogging. The rapid urbanization process in Hanoi has led, and will continue to lead, to the rapid growth of impervious surface areas.…”

Section: Regression Coefficient Of Gwr Modelsupporting

confidence: 93%

“…The stronger influence of impervious surface percentage on waterlogging frequency was located in the Dong Da, Thanh Xuan and Hoang Mai Districts (Figure 9e). This result agrees with a number of studies [12,26] which concluded that an As we know, rapid urbanization caused by population growth is accelerating the expansion of impervious surfaces, in addition to outdated underground drainage systems. Together, these two factors have probably played a critical role in increasing the waterlogging frequency in Hanoi.…”

Section: Regression Coefficient Of Gwr Modelsupporting

confidence: 92%

“…Urban streets act as streams by collecting rainwater, thereby increasing the volume of surface runoff, which is one of the most common causes of urban waterlogging [9][10][11]. In the context of climate change, waterlogging due to rainstorms is becoming more frequent and serious in developing countries and is causing losses to the economy, as well as affecting the living environment, people's daily life, and the sustainable development strategies of cities [12]. In 2017, Vietnam's urbanization exceeded 35% [13] and it is estimated to reach nearly 56% by 2050 [14].…”

Section: Introductionmentioning

confidence: 99%

“…Current research on urban waterlogging problems includes the causes of urban waterlogging [24][25][26][27][28], urban stormwater control [29,30], urban stormwater management [31][32][33][34][35][36], urban waterlogging simulation [37][38][39], and the spatial assessment of urban waterlogging risks and vulnerability in flood-prone areas [12,[40][41][42][43]. Some assessment models for urban waterlogging risk have been proposed.…”

Section: Introductionmentioning

confidence: 99%

“…[28,39,[44][45][46][47][48][49]. In addition, hydrologic-and hydrodynamic-based mathematical simulation models are among the main methods for evaluating urban waterlogging risks [12,37,40,41,50,51].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Tran

Dang

et al. 2020

Water

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In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

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Section: Regression Coefficient Of Gwr Modelsupporting

confidence: 93%

“…The stronger influence of impervious surface percentage on waterlogging frequency was located in the Dong Da, Thanh Xuan and Hoang Mai Districts (Figure 9e). This result agrees with a number of studies [12,26] which concluded that an As we know, rapid urbanization caused by population growth is accelerating the expansion of impervious surfaces, in addition to outdated underground drainage systems. Together, these two factors have probably played a critical role in increasing the waterlogging frequency in Hanoi.…”

Section: Regression Coefficient Of Gwr Modelsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Tran

Dang

et al. 2020

Water

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Risk assessment of urban rainstorm flood disaster based on land use/land cover simulation

Huang

Yang

et al. 2022

Hydrological Processes

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Under the urbanization context, land use/land cover (LULC) changes rapidly which affects both the probability of urban flood disaster and its consequence. In order to explore the impact of LULC changes on rainstorm flood disaster risk in the future, a framework of rainstorm flood disaster risk assessment is constructed from natural‐social perspective through LULC simulation based on the patch‐generating land use simulation (PLUS) model in this study. Specifically, the spatial LULC under three future development scenarios (inertia development scenario, economic priority scenario and sustainable development scenario) for the year 2030 is simulated, and then assessment model of rainstorm flood disaster risk is constructed through obtaining inundation depth and social vulnerability based on simulated LULC. Jingdezhen City in China is selected as an example. The results show that proposed risk assessment framework based on PLUS model can be used to detect the risk changes in different development scenarios. The risk of urban rainstorm flood disaster under economic priority scenario is most severe and risk under the sustainable development scenario is the lowest under the same return period rainstorm. Under the sustainable development scenario, green land including forest land, grassland and open water increases, which reduces the surface runoff and decrease the risk of rainstorm flood disaster effectively, however the reduction is limited with the greater rainstorm return period. Therefore, adaptive measures should be implemented together with land use planning to prevent and control urban flood risk. This study provides a framework to predict the future risk of urban rainstorm flood under different development scenarios, which can support for flood disaster risk prevention and urban sustainable development.

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Nature‐based solutions: Establishing a comprehensive framework for addressing urban waterlogging management

Zhao

Yue

2023

Integr Envir Assess & Manag

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Due to the impact of global climate change, many cities in China have been hit by severe rainstorms, leading to increasingly frequent urban waterlogging disasters. In recent years, nature‐based solutions (NbS) have received widespread attention and recognition, providing new ideas and approaches to addressing urban waterlogging issues. First, this article reviews the development process and concept of NbS and analyzes its core ideas and principles. Second, it analyzes the guiding role of NbS in urban waterlogging management and compares the commonalities and differences between NbS and three related waterlogging concepts. To ensure that urban waterlogging management is operational and dynamic, and that there is effective communication among stakeholders, this article proposes a comprehensive framework for the application of NbS in urban waterlogging management. Finally, this article analyzes the opportunities and potential of NbS applied to urban environmental issues. Integr Environ Assess Manag 2023;00:1–8. © 2023 SETAC

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A spatial assessment of urban waterlogging risk based on a Weighted Naïve Bayes classifier

Cited by 95 publications

References 28 publications

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Risk assessment of urban rainstorm flood disaster based on land use/land cover simulation

Nature‐based solutions: Establishing a comprehensive framework for addressing urban waterlogging management

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