ArticleScientific reports2024
Time series prediction model using LSTM-Transformer neural network for mine water inflow.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Evaluation of climate prediction models in Yunnan, China: traditional methods and AI approaches.Scientific reports · 2025Article
- Dynamic evolution of water conducting fracture zones and roof water hazard early warning based on 3d spatial clustering.Scientific reports · 2025Article
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Hybrid deep learning framework for real-time DO prediction in aquaculture.Scientific reports · 2025Article
- Modeling seawater intrusion along the Alabama coastline using physical and machine learning models to evaluate the effects of multiscale natural and anthropogenic stresses.Scientific reports · 2025Article
- Predictive model on employee stock ownership impacting corporate performance.Scientific reports · 2025Article
- Construction and application of optimized model for mine water inflow prediction based on neural network and ARIMA model.Scientific reports · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Mine flooding accidents have occurred frequently in recent years, and the predicting of mine water inflow is one of the most crucial flood warning indicators. Further, the mine water inflow is characterized by non-linearity and instability, making it difficult to predict. Accordingly, we propose a time series prediction model based on the fusion of the Transformer algorithm, which relies on self-attention, and the LSTM algorithm, which captures long-term dependencies. In this paper, Baotailong mine water inflow in Heilongjiang Province is used as sample data, and the sample data is divided into different ratios of the training set and test set in order to obtain optimal prediction results. In this study, we demonstrate that the LSTM-Transformer model exhibits the highest training accuracy when the ratio is 7:3. To improve the efficiency of search, the combination of random search and Bayesian optimization is used to determine the network model parameters and regularization parameters. Finally, in order to verify the accuracy of the LSTM-Transformer model, the LSTM-Transformer model is compared with LSTM, CNN, Transformer and CNN-LSTM models. The results prove that LSTM-Transformer has the highest prediction accuracy, and all the indicators of its model are well improved.
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