whilst linear scalings reveal the typical relationship between  and other optical/NUV/infrared (IR) properties *for a given sample*, they can not be used as a universal predictor of HI gas mass fraction, particularly in circumstances when galaxies are expected to deviate from the ‘normal’ relation (e.g. Cortese et al. 2011).

In this paper, we explore the application of non-linear methods, such as artificial neural networks (ANNs), to the challenge of gas fraction estimation. We have previously demonstrated the application of ANN to various astronomical problems, including pattern recognition for the spectral classification of galaxies (Teimoorinia 2012), fitting applications in order to estimate line fluxes which can be used for Active Galactic Nucleus (AGN) classifications, metal abundances etc. (Teimoorinia & Ellison 2014) and a large public catalog of estimated IR luminosities (Ellison et al. 2016a) that can be used to determined star formation rates for galaxies dominated by AGN (Ellison et al. 2016b). Most recently, we have presented a method for ranking the parameters involved in galaxy quenching, which effectively disentangles the complex multi-variate nature of this physical process (Teimoorinia et al. 2016). We refer the interested reader to those works for details of the general ANN method. The non-linear ANN techniques 