arXiv：cs.LG· Talha Ansar (University of Engineering and Technology, Lahore, Pakistan), Waqar Muhammad Ashraf (The Alan Turing Institute, London, UK), Alejandro Coca-Castro (The Alan Turing Institute, London, UK), Manvendra Janmaijaya (The Alan Turing Institute, London, UK), Ramit Debnath (University of Cambridge, Cambridge, UK), J. Scott Hosking (The Alan Turing Institute, London, UK)·· 11 小时前
英国气溶胶监测的数据驱动最优传感器布设框架
Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK
AI 导读
研究者开发了一套数据驱动传感器布设框架，并将其应用于英国的气溶胶监测。该框架将训练好的卷积高斯神经过程嵌入 DeepSensor 优化框架，遗传优化结合惰性求值与缓存将平均计算时间降低 60%（10±3 分钟对 25±7 分钟），布设性能与贪心求解器相当。
正文
Authors:Talha Ansar (1), Waqar Muhammad Ashraf (2), Alejandro Coca-Castro (2), Manvendra Janmaijaya (2), Ramit Debnath (3), J. Scott Hosking (2) ((1) University of Engineering and Technology, Lahore, Pakistan, (2) The Alan Turing Institute, London, UK, (3) University of Cambridge, Cambridge, UK)
Abstract:We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neural process is embedded within the DeepSensor optimisation framework. Genetic optimisation with lazy evaluation and caching reduces mean computational time by 60\% (10$\pm$3 versus 25$\pm$7 minutes) while producing sequential placement performance comparable to the greedy solver. Using the CAMS reference field to construct a retrospective oracle benchmark, Joint NLL improves by about 22\% for Joint-MI-driven genetic placement after five locations for aerosol stations are identified. Seasonal and wind-regime analyses show that agreement between model-based criteria and oracle benchmarks varies with meteorological transport conditions. These results show a computationally efficient approach for data-informed expansion of environmental observing systems.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.12658 [cs.LG] |
| (or arXiv:2610.12658v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12658 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Talha Ansar Ta [view email]
[v1]
Thu, 8 Oct 2026 19:25:38 UTC (1,478 KB)
来源：arXiv：cs.LG · arxiv.org