Data-driven Optimal Sensor Placement for Aerosol Monitoring in the UK
Talha Ansar (University of Engineering and TechnologyLahorePakistan)Waqar Muhammad Ashraf (The Alan Turing InstituteLondonUK)Alejandro Coca-Castro (The Alan Turing InstituteLondonUK)Manvendra Janmaijaya (The Alan Turing InstituteLondonUK)Ramit Debnath (University of CambridgeCambridgeUK)J. Scott Hosking (The Alan Turing InstituteLondonUK)
Oct 2026
Machine Learning
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.
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