Machine learning (ML) approaches can mine patterns that are difficult to draw intuitively from experimental datasets . Specifically, a function can be learned from a training dataset that contains the inputs and the desired outputs. An onboard assessment of water content could become a reality if a similar approach could be taken to marry an accurate assessment of water content in fuel cells with electrical signals.