Clean drinking water access is essential for public health and regarded as a scarce resource for Indigenous communities in rural and remote areas. In this research, a new iron and manganese prediction method based on Data Augmentation and Machine Learning Algorithms to be applied to drinking water in BC’s First Nation communities is reported. GAN based modelling and NIBS-NI based modelling were developed to investigate the effects of different data augmentation methods and predictors for iron and manganese prediction results. Reliable synthetic data was obtained through both data augmentation methods, allowing 4 machine learning algorithms to predict iron and manganese utilizing 3 and 5 physical properties respectively. Compared with RF, XGB, and DT machine learning models, the GBR model showed the strongest fitting ability and accurate predictions for both NI-BS-NI based modelling and GAN based modelling in predicting iron and manganese, with the Train R2 and Test R2 of two models nearing 1, and all the RMSE scores are below 0.06. The decision-making tool developed using GAN technology is considered to have greater application potential due to its ability to provide accurate predictions while requiring only 3 input physical parameters.
