  We present a simulation experiment of a pipeline based on machine learning
algorithms for neutral hydrogen (HI) intensity mapping (IM) surveys with
different telescopes. The simulation is conducted on HI signals, foreground
emission, thermal noise from instruments, strong radio frequency interference
(sRFI), and mild RFI (mRFI). We apply the Mini-Batch K-Means algorithm to
identify sRFI, and Adam algorithm to remove foregrounds and mRFI. Results show
that there exists a threshold of the sRFI amplitudes above which the
performance of our pipeline enhances greatly. In removing foregrounds and mRFI,
the performance of our pipeline is shown to have little dependence on the
apertures of telescopes. In addition, the results show that there are
thresholds of the signal amplitudes from which the performance of our pipeline
begins to change rapidly. We consider all these thresholds as the edges of the
signal amplitude ranges in which our pipeline can function well. Our work, for
the first time, explores the feasibility of applying machine learning
algorithms in the pipeline of IM surveys, especially for large surveys with the
next-generation telescopes.
