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abstract: 'The application of artificial neural networks (ANNs) for the estimation of HI gas mass fraction () is investigated, based on a sample of 13,674 galaxies in the Sloan Digital Sky Survey (SDSS) with HI detections or upper limits from the Arecibo Legacy Fast Arecibo L-band Feed Array (ALFALFA). We show that, for an example set of fixed input parameters ($g-r$ colour and $i$-band surface brightness), a multidimensional quadratic model yields  scaling relations with a smaller scatter (0.22 dex) than traditional linear fits (0.32 dex), demonstrating that non-linear methods can lead to an improved performance over traditional approaches. A more extensive ANN analysis is performed using 15 galaxy parameters that capture variation in stellar mass, internal structure, environment and star formation. Of the 15 parameters investigated, we find that $g-r$ colour, followed by stellar mass surface density, bulge fraction and specific star formation rate have the best connection with . By combining two control parameters, that indicate how well a given galaxy in SDSS is represented by the ALFALFA training set () and the scatter in the training procedure (), we develop a strategy for quantifying which SDSS galaxies our ANN can be adequately applied to, and the associated 