Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based on an improved autoencoder—the “Masked Autoencoder-based Classifier” model. This model, through a unique autoencoder framework and a custom ‘masked_loss’ loss function, achieves semi-supervised learning and dimensionality reduction of groundwater sample ionic data. By configuring the hidden layers, the classifier component of the model directly receives data processed by the encoder component. This not only improves the model's performance but also optimizes its complexity. Through an evaluation of the model's fitting effectiveness, our model achieved an average accuracy of 88.8% across 20 runs, with precision, recall, F1-score, and MCC reaching 88.1%, 80.6%, 0.827, and 0.833, respectively, significantly outperforming other classic models. The model successfully identified the sources of three sets of inrush water samples, with a high number of successful runs and clear average probabilities. This work contributes not only to the field of mine water inrush source identification but also offers a new perspective for the broader field of machine learning.