To this end, synchronous operando neutron imaging and EIS measurements were conducted in this study, manipulating the water content of the fuel cell by switching between dry and wet gases, which has previously been shown to provide a boost to fuel cell performance . Dual datasets are generated in which neutron measurements correspond to impedance spectra at different water content levels. Given the nonlinear relationship between impedance spectra and water content, an artificial neural network (ANN) was employed to establish this intricate relationship. Neutron imaging can provide accurate water distribution with good spatial and temporal resolution, while EIS is a universal, cost-effective tool in laboratories, a version of which could even be implemented into real-time and rapid onboard diagnostics of fuel cells . The integration of neutron imaging and EIS is intended to leverage the strengths of both techniques. By ensuring accurate correlation of the information provided by each method, we aim to develop a cheaper, more readily available, and onboard approach for real-time estimation of water content in a fuel cell stack.