Published: Jan. 1, 2024
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Language: Английский
Published: Jan. 1, 2024
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI
Language: Английский
Advances in public policy and administration (APPA) book series, Journal Year: 2024, Volume and Issue: unknown, P. 347 - 372
Published: Dec. 5, 2024
This chapter explores the transformative role of Artificial Intelligence (AI) in enhancing design, implementation, and management Blue-Green Infrastructure (BGI), a sustainable urban planning approach that integrates natural engineered systems to address environmental challenges. The convergence AI with BGI offers unprecedented opportunities improve resilience, optimize resource management, mitigate impacts climate change. Through advanced data analytics, predictive modeling, real-time monitoring, AI-driven solutions can enhance efficiency effectiveness projects. delves into various applications BGI, including smart water flood prediction prevention, heat island mitigation, biodiversity conservation. Case studies examples from global cities illustrate how is being leveraged create more adaptive, sustainable, resilient environments. also discusses challenges ethical considerations associated integration emphasizing need for interdisciplinary collaboration responsible deployment ensure equitable long-term benefits.
Language: Английский
Citations
11Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 112, P. 105597 - 105597
Published: June 20, 2024
Climate changes have led to increasing global energy consumption, detrimental the sustainable development of society. Urban blue-green infrastructure (UBGI) can improve urban microclimate. However, influence intensity UBGI on microclimate has not been quantified deeply use efficiency water and greenery resources. To solve research deficiencies, this study numerically simulated for 44 scenarios with different resource configurations (various body areas coverages) in summer. Based simulations, developed novel mathematical models thermo-environment (BGTE) quantify UBGI. The results indicated that daytime synergies first increased then decreased time. significance time (t), area (Sw), tree coverage rate (TCR), shrub (SCR), grassland (GLCR) synergy was by artificial neural network: t (39.4%), Sw (22.6%), TCR (22.0%), SCR (13.2%), GLCR (2.8%). make overall effect relatively efficient, should be less than 10000 m2, greater 65%, close 15%. This provides practical ideas efficient
Language: Английский
Citations
6Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105658 - 105658
Published: July 14, 2024
Language: Английский
Citations
6Building and Environment, Journal Year: 2023, Volume and Issue: 245, P. 110857 - 110857
Published: Sept. 22, 2023
Language: Английский
Citations
10Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 120, P. 106165 - 106165
Published: Jan. 24, 2025
Language: Английский
Citations
0Energy and Buildings, Journal Year: 2025, Volume and Issue: 332, P. 115421 - 115421
Published: Feb. 4, 2025
Language: Английский
Citations
0Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112726 - 112726
Published: Feb. 1, 2025
Language: Английский
Citations
0Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 127, P. 106418 - 106418
Published: May 12, 2025
Language: Английский
Citations
0Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106501 - 106501
Published: June 1, 2025
Language: Английский
Citations
0Energy and Buildings, Journal Year: 2024, Volume and Issue: 328, P. 115167 - 115167
Published: Dec. 9, 2024
Language: Английский
Citations
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