Abárzuza, F, Steele, IA, Jermak, H, Bento, J, Copley, D and Heffernan, D (2025) Astronomical Seeing Prediction with Machine Learning. Research Notes of the AAS, 9 (2). ISSN 2515-5172

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Abstract

We investigate the use of supervised machine learning models to predict current atmospheric seeing at the Observatorio del Roque de Los Muchachos, La Palma. The prediction is made from current, local meteorological conditions as measured by an on-site weather station. The model was trained over a six-month period from 2024 February 8 to 2024 July 3. We found that a simple random forest method provides better prediction than an multilayer perceptron neural network. The most important predictor is the maximum wind speed over a one-minute interval (0.23), followed by air pressure (0.18), wind speed standard deviation (0.17), relative humidity (0.16), air temperature (0.13), wind direction (0.10) and average wind speed (0.06). All other features had importance <0.05.

Item Type:	Article
Uncontrolled Keywords:	37 Earth Sciences; 3701 Atmospheric Sciences; Machine Learning and Artificial Intelligence
Subjects:	Q Science > QB Astronomy Q Science > QC Physics
Divisions:	Astrophysics Research Institute
Publisher:	American Astronomical Society
SWORD Depositor:	A Symplectic
Date Deposited:	14 Apr 2025 10:23
Last Modified:	14 Apr 2025 10:41
DOI or ID number:	10.3847/2515-5172/adb14f
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26176

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