Optimized green infrastructure planning at the city scale based on an interpretable machine learning model and multi-objective optimization algorithm: A case study of central Beijing, China

Landscape and Urban Planning, Journal Year: 2024, Volume and Issue: 252, P. 105191 - 105191

Published: Aug. 19, 2024

Language: Английский

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Attribution analysis of urban social resilience differences under rainstorm disaster impact: Insights from interpretable spatial machine learning framework

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106029 - 106029

Published: Dec. 1, 2024

Language: Английский

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Urban flood risk assessment and evacuation planning: a bi-level optimization model for sustainable high-density coastal areas

Annals of GIS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: Jan. 13, 2025

Flooding caused by extreme climate change is becoming increasingly severe, especially in high-density coastal areas worldwide. Although many studies have conducted risk assessments of urban floods, most not formed a comprehensive evacuation plan considering population distribution and flood disaster risk. To further enhance planning emergency management for areas, this study uses Victoria Harbor Hong Kong, typical flood-prone region, as research area. The first conducts exposure assessment classifies different regions according to levels. Then, combining ability with the changing road flows, novel bi-level optimization model proposed allocate zones citizens day night. With upper level using genetic algorithm minimize total system time lower applying user equilibrium evacuee allocation, forms an that considers hotspots impact risks on network. findings show functional high pedestrian flow, tourist spots, commercial centres, schools are exposed higher Besides, simulation matches zoning results actual activities can effectively achieve goal evacuating 480,000 people within 12–18 minutes. This innovatively proposes effective reference government's work.

Language: Английский

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Quantifying the Spatiotemporal Dynamics of Urban Flooding Susceptibility in the Greater Bay Area Under Shared Socio-Economic Pathways Using the Sd-Plus-Lightgbm Framework

Published: Jan. 1, 2025

Language: Английский

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Flood data platform governance: Identifying the technological and socio-technical approach(es) differences

Environmental Science & Policy, Journal Year: 2024, Volume and Issue: 162, P. 103938 - 103938

Published: Nov. 12, 2024

Language: Английский

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