Astronomical Seeing Prediction with Machine Learning

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.