  Atmospheric seeing is one of the most important parameters for evaluating and
monitoring an astronomical site. Moreover, being able to predict the seeing in
advance can guide observing decisions and significantly improve the efficiency
of telescopes. However, it is not always easy to obtain long-term and
continuous seeing measurements from a standard instrument such as differential
image motion monitor (DIMM), especially for those unattended observatories with
challenging environments such as Dome A, Antarctica. In this paper, we present
a novel machine learning-based framework for estimating and predicting seeing
at a height of 8 m at Dome A, Antarctica, using only the data from a
multi-layer automated weather station (AWS). In comparison with DIMM data, our
estimate has a root mean square error (RMSE) of 0.18 arcsec, and the RMSE of
predictions 20 minutes in the future is 0.12 arcsec for the seeing range from 0
to 2.2 arcsec. Compared with the persistence, where the forecast is the same as
the last data point, our framework reduces the RMSE by 37 percent. Our method
predicts the seeing within a second of computing time, making it suitable for
real-time telescope scheduling.
