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Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK
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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)

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ResearcharXiv cs.LG

Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK

arXiv:2610.12658v1 Announce Type: new 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.

Original source

This story was published by arXiv cs.LG and written by 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). SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on arxiv.org

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