Assessment of urban flood risk based on data-driven models: A case study in Fuzhou City, China

Ye, Chenlei; Xu, Zongxue; Lei, Xiaohui; Liao, Weihong; Ding, Xiangyi; Liang, Yongshuai

doi:10.1016/j.ijdrr.2022.103318

Cited by 9 publications

(2 citation statements)

References 39 publications

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“…This index not only captures trends in precipitation intensity at different time scales, but can also be used to assess the risk of natural disasters, such as floods, landslides, and mudslides caused by heavy rainfall during typhoons. The study by Ye et al [66] demonstrated that designing different flood inundation scenarios based on the statistical characteristics of historical precipitation can be applied to flood hazard risk mapping for urban planning. The TPCI-based assessment method proposed in our study is particularly effective in dealing with the non-stationary nature of typhoon precipitation [67], which is characterized by significant temporal variability.…”

Section: Discussionmentioning

confidence: 99%

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Qin

Lin

et al. 2023

Remote Sensing

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Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.

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Section: Discussionmentioning

confidence: 99%

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Qin

Lin

et al. 2023

Remote Sensing

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“…The monitoring and early warning of storm and flood disasters and risk management have been a common concern in the world for nearly half a century; many breakthroughs have been made in domestic and international research on storm and flood disaster risk assessment. Regarding the chronological order of application scenarios, existing storm and flood hazard assessments can be divided into three categories: pre-disaster assessments [ 10 ], mid-disaster follow-up monitoring and assessments, and post-disaster real-world assessments; in terms of research scales, there are studies that consider large areas and watersheds as units for overall regional risk assessment and centralised control [ 11 ]; there are also studies that consider the differences in the basic characteristics of specific cities [ 12 , 13 ]. From the perspective of research methods, most of the existing studies are based on statistical principles combined with 3S technologies [ 14 , 15 ], including hierarchical analysis, entropy value method, logistic regression method [ 16 ], BP neural network evaluation method, intelligent algorithms combined with RS-GIS technology [ 17 , 18 ], and hydrodynamic models [ 19 , 20 ], etc.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Liu

Zhou

Lin

et al. 2022

IJERPH

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Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.

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Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

Jin,

Zhao,

et al. 2024

Int J Disaster Risk Sci

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This study presents a novel method for optimizing parameters in urban flood models, aiming to address the tedious and complex issues associated with parameter optimization. First, a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model. Next, a principle for dividing urban hydrological response units was introduced, incorporating surface attribute features. The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model, and an artificial neural network (ANN) was employed to identify the sensitive parameters. Finally, a genetic algorithm (GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model. The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient (NSE) of 0.81. Compared to the ANN-GA and K-means-deep neural networks (DNN) methods, the proposed method better characterizes the runoff generation and flow processes. This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.

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Assessment of urban flood risk based on data-driven models: A case study in Fuzhou City, China

Cited by 9 publications

References 39 publications

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Using Machine Learning to Identify and Optimize Sensitive Parameters in Urban Flood Model Considering Subsurface Characteristics

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