The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities: A comprehensive systematic review

Environmental Science and Ecotechnology, Год журнала: 2024, Номер 20, С. 100433 - 100433

Опубликована: Май 17, 2024

In the dynamic landscape of sustainable smart cities, emerging computational technologies and models are reshaping data-driven planning strategies, practices, approaches, paving way for attaining environmental sustainability goals. This transformative wave signals a fundamental shift — marked by synergistic operation artificial intelligence (AI), things (AIoT), urban digital twin (UDT) technologies. While previous research has largely explored AI, AIoT, UDT in isolation, significant knowledge gap exists regarding their interplay, collaborative integration, collective impact on context cities. To address this gap, study conducts comprehensive systematic review to uncover intricate interactions among these interconnected technologies, models, domains while elucidating nuanced dynamics untapped synergies complex ecosystem Central four guiding questions: What theoretical practical foundations underpin convergence UDT, planning, how can components be synthesized into novel framework? How does integrating AI AIoT reshape improve performance cities? augment capabilities enhance processes challenges barriers arise implementing what strategies devised surmount or mitigate them? Methodologically, involves rigorous analysis synthesis studies published between January 2019 December 2023, comprising an extensive body literature totaling 185 studies. The findings surpass mere interdisciplinary enrichment, offering valuable insights potential advance development practices. By enhancing processes, integrated offer innovative solutions challenges. However, endeavor is fraught with formidable complexities that require careful navigation mitigation achieve desired outcomes. serves as reference guide, spurring groundbreaking endeavors, stimulating implementations, informing strategic initiatives, shaping policy formulations sustainable, development. These have profound implications researchers, practitioners, policymakers, providing roadmap fostering resiliently designed, technologically advanced, environmentally conscious environments.

Язык: Английский

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Algorithmic green infrastructure optimisation: Review of artificial intelligence driven approaches for tackling climate change

Sustainable Cities and Society, Год журнала: 2024, Номер 101, С. 105182 - 105182

Опубликована: Янв. 7, 2024

Green infrastructure (GI) is a fundamental building block of our cities. It contributes to the sustainability and vitality cities by offering various benefits such as greening, cooling, water, air quality, managing carbon emissions. GI plays an essential role in enhancing overall well-being. The utilisation artificial intelligence (AI) technologies for optimisation perceived powerful approach A knowledge gap, nevertheless, remains research on AI-driven tackling climate change. This study aims consolidate comprehension optimisation, particularly methodology adopts PRISMA protocol perform systematic literature review. review results are analysed from six aspects—i.e., objectives, objectives categories, indicators, models, types, scales. findings revealed: (a) was mainly undertaken areas biodiversity ecosystem security, energy efficiency, public health, heat islands, water management; (b) Indicator categories were concentrated indicators related GI, objective, other general/supporting indicators. Based these findings, framework developed enhance understanding process within realm

Язык: Английский

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Artificial intelligence of things for synergizing smarter eco-city brain, metabolism, and platform: Pioneering data-driven environmental governance

Sustainable Cities and Society, Год журнала: 2024, Номер 108, С. 105516 - 105516

Опубликована: Май 9, 2024

Emerging smarter eco-cities, inherently intertwined with environmental governance, function as experimental sites for testing novel technological solutions and implementing reforms aimed at addressing complex challenges. However, despite significant progress in understanding the distinct roles of emerging data-driven governance systems—namely City Brain, Smart Urban Metabolism (SUM), platform urbanism—enabled by Artificial Intelligence Things (AIoT), a critical gap persists systematically exploring untapped potential stemming from their synergistic collaborative integration context urban governance. To fill this gap, study aims to explore linchpin AIoT seamlessly integrating these systems advance eco-cities. Specifically, it introduces pioneering framework that effectively leverages synergies among AIoT-powered enhance sustainability practices In developing framework, employs configurative aggregative synthesis approaches through an extensive literature review in-depth case analysis publications spanning 2018 2023. The identifies key factors driving co-evolution AI IoT into specifies technical components constituting architecture A comparative reveals commonalities differences SUM, urbanism within frameworks These collectively contribute eco-cities leveraging real-time data analytics, predictive modeling, stakeholder engagement. proposed underscores importance decision-making, optimization resource management, reduction impact, collaboration stakeholders, engagement citizens, formulation evidence-based policies. findings unveils presents promising opportunities prospects advancing not only charts strategic trajectory stimulating research endeavors but also holds practical application informed policymaking realm ongoing discussions refinements remain imperative address identified challenges, ensuring framework's robustness, ethical soundness, applicability across diverse contexts.

Язык: Английский

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Artificial intelligence and the local government: A five-decade scientometric analysis on the evolution, state-of-the-art, and emerging trends

Cities, Год журнала: 2024, Номер 152, С. 105151 - 105151

Опубликована: Июнь 8, 2024

Язык: Английский

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Towards multi-scale and context-specific heat health risk assessment - A systematic review

Sustainable Cities and Society, Год журнала: 2025, Номер 119, С. 106102 - 106102

Опубликована: Янв. 5, 2025

Язык: Английский

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Generative Spatial Artificial Intelligence for Sustainable Smart Cities: A Pioneering Large Flow Model for Urban Digital Twin

Environmental Science and Ecotechnology, Год журнала: 2025, Номер 24, С. 100526 - 100526

Опубликована: Янв. 15, 2025

Язык: Английский

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Residents’ seasonal behavior patterns and spatial preferences in public open spaces of severely cold regions: Evidence from Harbin, China

Habitat International, Год журнала: 2025, Номер 156, С. 103279 - 103279

Опубликована: Янв. 7, 2025

Язык: Английский

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Unlocking Artificial Intelligence Adoption in Local Governments: Best Practice Lessons from Real-World Implementations

Smart Cities, Год журнала: 2024, Номер 7(4), С. 1576 - 1625

Опубликована: Июнь 28, 2024

In an era marked by rapid technological progress, the pivotal role of Artificial Intelligence (AI) is increasingly evident across various sectors, including local governments. These governmental bodies are progressively leveraging AI technologies to enhance service delivery their communities, ranging from simple task automation more complex engineering endeavours. As governments adopt AI, it imperative understand functions, implications, and consequences these advanced technologies. Despite growing importance this domain, a significant gap persists within scholarly discourse. This study aims bridge void exploring applications context government provision. Through inquiry, seeks generate best practice lessons for smart city initiatives. By conducting comprehensive review grey literature, we analysed 262 real-world implementations 170 worldwide. The findings underscore several key points: (a) there has been consistent upward trajectory in adoption over last decade; (b) China, US, UK at forefront adoption; (c) among technologies, natural language processing robotic process emerge as most prevalent ones; (d) primarily deploy 28 distinct services; (e) information management, back-office work, transportation traffic management leading domains terms adoption. enriches existing body knowledge providing overview current sphere governance. It offers valuable insights policymakers decision-makers considering adoption, expansion, or refinement urban Additionally, highlights using guide successful integration optimisation future projects, ensuring they meet evolving needs communities.

Язык: Английский

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Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework

Applied Sciences, Год журнала: 2024, Номер 14(13), С. 5501 - 5501

Опубликована: Июнь 25, 2024

Local governments face critical challenges in the era of digital transformation, balancing responsibility safeguarding resident information and administrative documents while maintaining data integrity public trust. These responsibilities become even more as they transition into smart cities adopting advanced technological innovations to revolutionize governance, enhance service delivery, foster sustainable resilient urban environments. Technological advancements like Internet-of-Things devices artificial intelligence-driven approaches can provide better services residents, but also expose local cyberthreats. There has been, nonetheless, very little study on cybersecurity issues from government perspective, multifaceted nature settings is scattered fragmented, highlighting need for a conceptual understanding adequate action. Against this backdrop, aims identify key components governmental context through systematic literature review. This review further extends development framework providing comprehensive government’s landscape. makes significant contribution academic professional domains policies within context, offering valuable insights decision-makers, practitioners, academics. helps vulnerabilities, enabling stakeholders recognize shortcomings their implement effective countermeasures safeguard confidential documents. Thus, findings inform policy cybersecurity-aware prepared.

Язык: Английский

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Predictive maintenance management of gear systems in the era of computer vision

International Journal of Quality & Reliability Management, Год журнала: 2025, Номер unknown

Опубликована: Янв. 8, 2025

Purpose This paper proposes a framework to assist in managing predictive maintenance by detecting progressive surface wear on spur gears through the analysis of digital images gear teeth using computer vision (CV) techniques. Design/methodology/approach An experimental setup was constructed capture endoscopic cameras. The were selected, pre-processed, stored database and used study proposed framework. Three CV techniques explored within for gears: (1) edge detection; (2) gray level co-occurrence matrix (GLCM) combined with machine learning (ML) algorithms (3) deep convolutional neural networks (CNN). Findings results showed 85% accuracy detection algorithm. Among ML algorithms, above 60% support vector (SVM) 70% K-nearest neighbors (KNN). Principal component (PCA) indicated that as distance between principal components increased, it characterized formation progression teeth. With CNN, an 99.999981% achieved training loss rate, classification rate (CAR) 91.6666%, F1 score 90.9090% recall 83.3334% during testing phase. Practical implications is applicable variety systems industrial contexts requiring maintenance, making highly scalable solution industry professionals. Originality/value novel considers various detect assess surfaces. Moreover, provide guidelines selecting most appropriate method systems.

Язык: Английский

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