Remote Sensing, Multi-Objective Optimisation, and Agent-Based Simulation for Urban Nature-Based Flood Mitigation

Autores/as

DOI:

https://doi.org/10.66838/sauc.6441

Palabras clave:

Nature-based solutions, Multi-objective optimization, Reinforcement learning, Agent-based flood simulation, Remote sensing, NSGA II, Urban flood resilience

Resumen

By 2030, pluvial flooding is estimated to cause nearly $500 billion in global damage, affecting 54 million people (UNDRR, 2015) each year. By 2050, metropolitan assets worth around $158 trillion could be at risk of floods. This rising risk has sparked an increased interest in nature-based solutions (NbS), as traditional hydraulic infrastructure may lack the flexibility required to manage more complex flood hazards. This study looks into the use of a data-driven approach to identify and assess the effectiveness of upstream afforestation in preventing pluvial floods. We constructed a thorough computational framework for the Girona/Empordà region of Catalonia, Spain....

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Publicado

2026-10-15

Cómo citar

Yashashvi Agrawal, Marcos Navarrete Pardo, Ntagkouli, E., & Pachchigar, S. (2026). Remote Sensing, Multi-Objective Optimisation, and Agent-Based Simulation for Urban Nature-Based Flood Mitigation. Street Art & Urban Creativity, 12(5), 745–756. https://doi.org/10.66838/sauc.6441

Número

Vol. 12 Núm. 5 (2026)

Sección

Artículos de investigación

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