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Abstract Data for Policy (dataforpolicy.org), a trans-disciplinary community of research and practice, has emerged around the application and evaluation of data technologies and analytics for policy and governance. Research in this area has involved cross-sector collaborations, but the areas of emphasis have previously been unclear. Within the Data for Policy framework of six focus areas, this report offers a landscape review of Focus Area 2: Technologies and Analytics. Taking stock of recent advancements and challenges can help shape research priorities for this community. We highlight four commonly used technologies for prediction and inference that leverage datasets from the digital environment: machine learning (ML) and artificial intelligence systems, the internet-of-things, digital twins, and distributed ledger systems. We review innovations in research evaluation and discuss future directions for policy decision-making.
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Drone‐based digital twins for water quality monitoring: A systematic review
Abstract The rapid advancement of drone technology and digital twin systems has significantly transformed environmental monitoring, particularly in the field of water quality assessment. This paper systematically reviews the current state of research on the application of drones, digital twins, and their integration for water quality monitoring and management. It highlights key themes, insights, research trends, commonly used methodologies, and future directions from existing studies, aiming to provide a foundational reference for further research to harness the promising potential of these technologies for effective, scalable solutions in water resource management, addressing both immediate and long‐term environmental challenges. The systematic review followed PRISMA guidelines, rigorously analysing hundreds of relevant papers. Key findings emphasise the effectiveness of drones in capturing real‐time, high‐resolution spatial and temporal data, as well as the value of digital twins for predictive and simulation‐based analysis. Most importantly, the review demonstrates the potential of integrating these technologies to enhance sustainable water management practices. However, it also identifies a significant research gap in fully integrating drones with digital twins for comprehensive water quality management. In response, the review outlines future research directions, including improvements in data integration techniques, predictive models, and interdisciplinary collaboration.
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- PAR ID:
- 10561932
- Publisher / Repository:
- DOI PREFIX: 10.1049
- Date Published:
- Journal Name:
- Digital Twins and Applications
- Volume:
- 1
- Issue:
- 2
- ISSN:
- 2995-5629
- Format(s):
- Medium: X Size: p. 131-160
- Size(s):
- p. 131-160
- Sponsoring Org:
- National Science Foundation
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