[Submitted on 8 Oct 2026]
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.
Submission history
From: Talha Ansar Ta [view email]
[v1] Thu, 8 Oct 2026 19:25:38 UTC (1,478 KB)