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Updated: Jun 18, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach.

Jiaxin Bian1,2,3, Tao Hou4, Dengjun Ren4

  • 1School of Water and Environment, Chang'an University, Xi'an, 710064, China.

Scientific Reports
|August 1, 2024
PubMed
Summary

A new model combining CEEMDAN, NGO, and LSTM accurately predicts sudden mine water inflow changes. This advanced method improves safety monitoring in smart mines.

Keywords:
CEEMDANDeep learning modelsLSTMMine water inflowNGOShort-term prediction

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Area of Science:

  • Geosciences and Environmental Science
  • Artificial Intelligence and Machine Learning
  • Mining Engineering

Background:

  • Mine water inflows pose significant risks to coal mining safety, especially in deep operations.
  • Accurate prediction of sudden water inflow changes is challenging due to complex hydrogeological parameters and limitations of traditional models.
  • Existing singular machine learning approaches struggle with forecasting abrupt shifts in mine water inflow volumes.

Purpose of the Study:

  • To develop and evaluate a novel coupled decomposition-optimization-deep learning model for enhanced mine water inflow prediction.
  • To compare the performance of singular, decomposition-prediction, and decomposition-optimization-prediction coupled models in capturing sudden changes.
  • To provide a robust technical solution for safety monitoring in smart mines.

Main Methods:

  • Integration of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Northern Goshawk Optimization (NGO), and Long Short-Term Memory (LSTM) networks.
  • Evaluation of three forecasting approaches: singular time series prediction, CEEMDAN-LSTM, and CEEMDAN-NGO-LSTM.
  • Assessment of prediction accuracy and ability to capture sudden changes in mine water inflow data.

Main Results:

  • The CEEMDAN-NGO-LSTM model significantly outperforms singular prediction and CEEMDAN-LSTM models in predicting extreme shifts.
  • The CEEMDAN-NGO-LSTM model achieved MAE of 96.578, MAPE of 1.471%, RMSE of 122.143, and NSE of 0.958.
  • This coupled model demonstrated average performance improvements of 44.950% and 19.400% over LSTM and CEEMDAN-LSTM, respectively.
  • The model provided the most accurate 5-day ahead predictions of mine water inflow volumes.

Conclusions:

  • The proposed CEEMDAN-NGO-LSTM model offers a superior approach for forecasting mine water inflow, especially abrupt changes.
  • This decomposition-optimization-prediction coupled model enhances safety monitoring capabilities in smart mining environments.
  • The study provides significant theoretical and practical value for ensuring safe and efficient coal mining operations.