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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Risk assessment of flood disasters in the Poyang lake area

International Journal of Disaster Risk Reduction, Journal Year: 2023, Volume and Issue: 100, P. 104208 - 104208

Published: Dec. 20, 2023

Language: Английский

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Vulnerability assessment of urban rail transit in face of disruptions: A framework and some lessons from Hong Kong

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104858 - 104858

Published: Aug. 8, 2023

Language: Английский

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Advancing understanding of the complex nature of flood risks to inform comprehensive risk management: Findings from an urban region in Central Vietnam

International Journal of Disaster Risk Reduction, Journal Year: 2024, Volume and Issue: 110, P. 104652 - 104652

Published: July 2, 2024

Driven by rapid climate, socio-economic, environmental, and political change, flood risks in urban regions are on the rise. Given that cities highly complex integrated systems comprising social, ecological infrastructure domains, characterized high levels of complexity, such as cascading effects, interconnected interacting risk drivers. To ensure effectiveness management interventions, enhanced understanding empirical evidence nature is needed. Failing to understand how interact across systems, not identifying interactions underlying drivers root causes can lead maladaptation planning. Addressing this, we use impact chains webs, i.e. conceptual models have been co-created validated a participatory manner, break down risks, using flood-prone region Hue Central Vietnam case study. Results show impacts deeply interconnected, with effects systems. Further, our analysis reveals induced same causes. The co-development provides useful methodology move from systemic management.

Language: Английский

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Study on multiscale-multivariate prediction and risk assessment of urban flood

Environmental Modelling & Software, Journal Year: 2024, Volume and Issue: 173, P. 105958 - 105958

Published: Jan. 13, 2024

Language: Английский

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Simulating flood risk in Tampa Bay using a machine learning driven approach

npj natural hazards., Journal Year: 2024, Volume and Issue: 1(1)

Published: Dec. 6, 2024

Machine learning (ML) models can simulate flood risk by identifying critical non-linear relationships between damage locations and factors (FRFs). To explore it, Tampa Bay, Florida, is selected as a test site. The study's goal to identify dominant FRFs using historical data target variable, with 16 predictor variables. Five different ML such decision tree (DT), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient (XGBoost), random forest (RF) were adopted. RF classifies 2.42% of Bay very high 2.54% risk, while XGBoost 3.85% 1.11% risk. Moreover, the communities reside at low altitudes near waterbodies, dense man-made infrastructure, are This study introduces comprehensive framework for assessment helps policymakers mitigate

Language: Английский

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Technological Advances in Flood Risk Assessment and Related Operational Practices Since the 1970s: A Case Study in the Pikrodafni River of Attica

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 112 - 112

Published: Jan. 3, 2025

As cities have expanded into floodplains, the need for their protection has become crucial, prompting evolution of flood studies. Here, we describe operational tools, methods and processes used in risk engineering studies 1970s, evaluate technological progress up to present day. To this aim, reference relevant regulations legislation recorded experiences engineers who performed hydrological, surveying hydraulic 1970s. These are compared with framework a contemporary assessment study conducted Pikrodafni basin Attica region. We conclude that, without technologically advanced tools available today, achieving level detail accuracy mapping that is now possible would been unfeasible, even significant human resources. However, ongoing urban development growth continue encroach upon plains existed centuries, contributing increased risk.

Language: Английский

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Comprehensive risk assessment of high-temperature disasters affecting rice production in Shanghai

Ecological Indicators, Journal Year: 2025, Volume and Issue: 171, P. 113160 - 113160

Published: Jan. 28, 2025

Language: Английский

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Flood risk modelling by the synergistic approach of machine learning and best-worst method in Indus Kohistan, Western Himalaya

Geomatics Natural Hazards and Risk, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 25, 2025

Language: Английский

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Integrating river channel flood diversion strategies into dynamic urban flood risk assessment and multi-objective optimization of emergency shelters

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(3)

Published: March 1, 2025

With the continuous advancement of urbanization, risk urban flooding is increasing, making establishment emergency shelters crucial for mitigating flood disasters. This study uses Jinshui River diversion pipeline project in Zhengzhou as a case to systematically investigate effect measures on reducing risks and optimize site selection based assessments. First, InfoWorks integrated catchment management model used simulate under different rainfall scenarios. Second, integrating multi-source data, technique order preference by similarity an ideal solution with four weighting methods applied identify high-risk areas. Finally, results assessment are weights multi-objective model, which solved particle swarm optimization algorithm determine optimal shelter locations. The show that: (1) In 10, 50, 200-years scenarios, significantly reduce depth inundated areas; however, limited extreme “7·20” event. (2) High-risk areas primarily concentrated highly urbanized northeast, although alleviates risk, overall remains high events. (3) Under scenario after diversion, 13 locations identified, average evacuation distance 471.9 meters, covering 97.3% population area. These findings provide scientific evidence management.

Language: Английский

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