TPE-LightGBM Model Pinpoints Water Hazards in Coal Mines

By Muhammad OsamaReviewed by Susha Cheriyedath, M.Sc.Jun 10 2024

In a recent article published in the journal Scientific Reports, researchers introduced an innovative water source identification model called a tree-structured Parzen estimator and light gradient booster machine (TPE-LightGBM). Their primary objective was to address the difficulties associated with precisely pinpointing the source of abrupt water hazards in complex hydrogeological settings, which is essential for effectively preventing and managing water-related issues in coal mines.

Background

Water is vital for human life and industrial progress, yet it poses significant risks to coal mining safety. Events such as inrushes, flooding, and bursting can cause severe accidents and economic losses in coal mines. Therefore, accurately identifying mine water sources and monitoring their quality and quantity is crucial. However, this task is challenging due to the complexity and varied sources of water in mines. Additionally, mine water's chemical composition can vary based on geological conditions, mining activities, and environmental factors.

Conventional Approach

Identifying mine water sources typically involves employing machine learning models that learn from data and make predictions based on various features. These models are categorized into two types: supervised and unsupervised. Supervised techniques rely on labeled data, where each sample's water source type is known and provided as the target variable.

In contrast, unsupervised models do not require labeled data but can cluster samples into groups based on feature similarity. However, both types of models have limitations, including the risk of overfitting, underfitting, and high computational costs.

About the Research

In this paper, the authors proposed a novel machine learning model, TPE-LightGBM, for water source identification, which integrates the strengths of both supervised and unsupervised models. This model harnesses the adaptive parameter-seeking capabilities of the TPE algorithm and the fast gradient-boosting framework of LightGBM.

The TPE algorithm, a Bayesian optimization technique, dynamically adjusts the search space parameters to efficiently find the optimal solution in a limited number of iterations. Meanwhile, LightGBM constructs decision trees using the gradient descent method, making it a rapid and effective gradient-boosting framework.

The study utilized the TPE algorithm to fine-tune LightGBM parameters such as the number of trees, learning rate, and maximum depth. By optimizing these parameters, the LightGBM model effectively classified water samples into distinct categories based on their chemical compositions, including potential hydrogen (pH), total dissolved solids (TDS), calcium ion (Ca²⁺), and magnesium ion (Mg²⁺). TDS serves as an indicator of water salinity and pollution levels, while pH measures the acidity or alkalinity of water, influencing the solubility and mobility of metals and minerals within the water body.

Furthermore, the developed method was applied to a case study in a Chinese coal mine, where 120 water samples from various locations were collected and analyzed for chemical components. These samples were categorized into four water source types: surface water, groundwater, fissure water, and mixed water.

Additionally, the proposed technique was compared with two other supervised models, a support vector machine (SVM) and a random forest (RF). The researchers employed 10-fold cross-validation and four evaluation metrics, including accuracy, precision, recall, and F1-score, to further assess the models' performance.

Research Findings

The outcomes showed that the TPE-LightGBM model surpassed the other two models across all metrics, achieving an accuracy, precision, recall, and F1 score of 98.33%. In comparison, the SVM model achieved 95.83% accuracy, 96.15% precision, 95.83% recall, and a 95.97% F1 score. The RF model attained an accuracy of 93.33%, a precision of 93.75%, a recall of 93.33%, and an F1-score of 93.51%.

Moreover, the study assessed the generalization error of the models to measure their performance on unseen data. The TPE-LightGBM model exhibited the lowest generalization error of 0.0167, whereas the SVM model showed a generalization error of 0.0417, and the RF model presented a generalization error of 0.0667.

Furthermore, the authors analyzed to determine the contribution of each variable to the classification process. They found that Ca²⁺ had the most significant impact, followed by Mg²⁺, TDS, and pH. They explained that Ca²⁺ and Mg²⁺ served as key components influencing water hardness, reflecting the extent of water-rock interaction.

Conclusion

In summary, the novel approach effectively identified the source of sudden water hazards in the Coalfield, making a crucial contribution to enhancing coal mine safety and productivity. Moreover, its potential applications extend beyond mining to encompass environmental monitoring, water resource management, and water quality assessment.

Moving forward, future work could concentrate on expanding the sample database, refining the model's generalization performance, and integrating additional methodologies such as hydrogeology and hydrochemistry for further advancement.