Fast simulation and prediction of urban pluvial floods using a deep convolutional neural network model

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 624, P. 129945 - 129945

Published: July 18, 2023

Language: Английский

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An XGBoost-SHAP approach to quantifying morphological impact on urban flooding susceptibility

Ecological Indicators, Journal Year: 2023, Volume and Issue: 156, P. 111137 - 111137

Published: Oct. 29, 2023

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected city configurations. However, explainable Artificial Intelligence could provide insights into how urban configurations flooding. This study, taking entered on Shenzhen City, deploys an XGBoost, integrating SHapley Additive exPlanation and Partial Dependency Plots, to assess morphology influences susceptibility. The models strategies presented in this study aimed adapt extreme storms from the perspective of spatial configuration planning. findings underscore varying impact disaster variables flooding, with morphological attributes becoming highly significant during severe inundations. In analysis, mean building volume emerged as a pivotal parameter, SHAP value 0.0107 m contribution ratio 9.70 %. indicates that should optimized minimize risks. It is recommended Mean Building Volume (MBV) maintained within range 1.25 km3 2.5 km3, Standard Deviation (SDBV) kept below 2.814 km3. By harnessing algorithms, offers intricate relationship between forms flood risk, thereby informing development effective adaptation strategies.

Language: Английский

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Assessment of the urban waterlogging resilience and identification of its driving factors: A case study of Wuhan City, China

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 866, P. 161321 - 161321

Published: Jan. 2, 2023

Language: Английский

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Simulation Performance Evaluation and Uncertainty Analysis on a Coupled Inundation Model Combining SWMM and WCA2D

International Journal of Disaster Risk Science, Journal Year: 2022, Volume and Issue: 13(3), P. 448 - 464

Published: June 1, 2022

Abstract Urban floods are becoming increasingly more frequent, which has led to tremendous economic losses. The application of inundation modeling predict and simulate urban flooding is an effective approach for disaster prevention risk reduction, while also addressing the uncertainty problem in model always a challenging task. In this study, cellular automaton (CA)-based combining storm water management (SWMM) weighted automata 2D was applied physical-based (LISFLOOD-FP) coupled with SWMM comparison. simulation performance factors were systematically discussed. results show that CA-based can achieve sufficient accuracy higher computational efficiency than model. resolution terrain rainstorm data had strong influence on model, simulations would be less creditable when using input lower 15 m recorded interval rainfall greater 30 min. roughness value type showed limited impacts change depth occurrence peak area. Generally, demonstrated laudable applicability recommended fast flood episodes. This study provide references implications reducing constructing

Language: Английский

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A novel spatial optimization approach for the cost-effectiveness improvement of LID practices based on SWMM-FTC

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 307, P. 114574 - 114574

Published: Jan. 24, 2022

Language: Английский

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Assessment of vulnerability to waterlogging in subway stations using integrated EWM-TOPSIS

Smart Construction and Sustainable Cities, Journal Year: 2023, Volume and Issue: 1(1)

Published: Nov. 14, 2023

Abstract Waterlogging in subway stations has a devastating impact on normal operation of important urban facilities and can cause harm to passengers property. It is difficult assess the vulnerability metro waterlogging because many complex factors are involved. This study proposes hybrid model by integrating entropy weight method (EWM) with technique for order preference based similarity ideal solution (TOPSIS) (the EWM-TOPSIS method). The analysis influencing waterlogging. proposed was applied field case (Jinshahu station Hangzhou, found be vulnerable at level IV). results from EWM-TOPSIS, EWM, TOPSIS were compared. using more accurate reliable than those EWM TOPSIS. However, reliability determined historical data, which cannot capture rapidly changing factors. Based assessment results, recommendations made promote overall health development areas satisfy United Nations Sustainable Development Goal 11 (SDG11).

Language: Английский

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Assessing and enhancing urban road network resilience under rainstorm waterlogging disasters

Transportation Research Part D Transport and Environment, Journal Year: 2023, Volume and Issue: 123, P. 103928 - 103928

Published: Oct. 1, 2023

Language: Английский

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Urban waterlogging susceptibility assessment based on hybrid ensemble machine learning models: A case study in the metropolitan area in Beijing, China

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 630, P. 130695 - 130695

Published: Jan. 23, 2024

Language: Английский

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Flood risk assessment of urban metro system using random forest algorithm and triangular fuzzy number based analytical hierarchy process approach

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 109, P. 105546 - 105546

Published: May 21, 2024

Language: Английский

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Resilience assessment of subway system to waterlogging disaster

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105710 - 105710

Published: July 26, 2024

Language: Английский

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