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Research on mining land subsidence by intelligent hybrid model based on gradient boosting with categorical features
Biao Zhang1, Chun Xu1, Xingguo Dai1
1School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China.
Journal of Environmental Management
|February 20, 2024
Summary
This study introduces hybrid machine learning models to predict coal mining-induced land subsidence (MLS). The Sparrow Search Algorithm combined with CatBoost (SSA-CatBoost) significantly improved prediction accuracy, offering a reliable method for environmental and safety management.
Area of Science:
- Geosciences and Environmental Science
- Artificial Intelligence and Machine Learning
Background:
- Coal mining-induced land subsidence (MLS) presents significant risks to infrastructure and ecosystems.
- Accurate prediction of MLS is complex and relies heavily on researcher expertise.
Purpose of the Study:
- To develop and evaluate hybrid machine learning models for predicting MLS.
- To compare the performance of five intelligent optimization algorithms integrated with the CatBoost model.
Main Methods:
- Development of five hybrid models: Ant Lion Optimizer-CatBoost (ALO-CatBoost), Bald Eagle Search-CatBoost (BES-CatBoost), Bird Swarm Algorithm-CatBoost (BSA-CatBoost), Harris Hawks Optimization-CatBoost (HHO-CatBoost), and Sparrow Search Algorithm-CatBoost (SSA-CatBoost).
- Comparative analysis of prediction accuracy and reliability using metrics like R², RMSE, MAE, and VAF.
- Utilizing the Shapley method to analyze feature importance and contribution to predictions.
Main Results:
- All hybrid models demonstrated improved prediction performance over single models.
- The SSA-CatBoost model achieved the most significant improvement, with R² increasing from 0.927 to 0.965.
- Feature importance analysis provided insights into factors influencing MLS predictions.
Conclusions:
- Hybrid model technology offers a reliable approach for predicting land subsidence caused by coal mining.
- This research provides a valuable tool for mining technicians to assess MLS impacts and inform safety and environmental management strategies.
Keywords:
Coal miningIntelligent algorithmsMachine learningShapley additive explanationsSubsidence predictionMore Related Videos
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