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Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China

Utilisation d’une forêt aléatoire pour l’évaluation des risques liés à l’irruption de l’eau dans le charbon de la mine de charbon de Panjiayao, dans le nord de la Chine

Usando Bosque Aleatorio para la evaluación de riesgo de afluencias de agua en mina Panjiayao, una mina de carbón en el Norte de China

基于随机森林的中国北方潘家窑煤矿煤层底板突水危险性评价

Uso de floresta aleatória para a avaliação do risco de inrush da água do piso de carvão na mina de carvão de Panjiayao, norte da China

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Abstract

Coal-floor water-inrush incidents account for a large proportion of coal mine disasters in northern China, and accurate risk assessment is crucial for safe coal production. A novel and promising assessment model for water inrush is proposed based on random forest (RF), which is a powerful intelligent machine-learning algorithm. RF has considerable advantages, including high classification accuracy and the capability to evaluate the importance of variables; in particularly, it is robust in dealing with the complicated and non-linear problems inherent in risk assessment. In this study, the proposed model is applied to Panjiayao Coal Mine, northern China. Eight factors were selected as evaluation indices according to systematic analysis of the geological conditions and a field survey of the study area. Risk assessment maps were generated based on RF, and the probabilistic neural network (PNN) model was also used for risk assessment as a comparison. The results demonstrate that the two methods are consistent in the risk assessment of water inrush at the mine, and RF shows a better performance compared to PNN with an overall accuracy higher by 6.67%. It is concluded that RF is more practicable to assess the water-inrush risk than PNN. The presented method will be helpful in avoiding water inrush and also can be extended to various engineering applications.

Résumé

Les incidents liés à l’irruption de l’eau dans le charbon représentent une grande partie des catastrophes causées par les mines de charbon dans le nord de la Chine, et une évaluation précise des risques est cruciale pour la production de charbon en toute sécurité. Un modèle d’évaluation novateur et prometteur pour l’appel d’eau est proposé basé sur la forêt aléatoire (RF), qui est un puissant algorithme intelligent d’apprentissage automatique. Les RF présentent des avantages considérables, notamment une précision de classification élevée et la capacité d’évaluer l’importance des variables; en particulier, il résiste aux problèmes compliqués et non linéaires inhérents à l’évaluation des risques. Dans cette étude, le modèle proposé est appliqué à la mine de charbon de Panjiayao, dans le nord de la Chine. Huit facteurs ont été sélectionnés comme indices d’évaluation en fonction de l’analyze systématique des conditions géologiques et d’une étude de terrain de la zone d’étude. Des cartes d’évaluation des risques ont été générées sur la base de RF, et le modèle de réseau neuronal probabiliste (PNN) a également été utilisé pour l’évaluation des risques en tant que comparaison. Les résultats démontrent que les deux méthodes sont cohérentes dans l’évaluation du risque d’irruption de l’eau à la mine, et que la RF montre une meilleure performance par rapport au PNN avec une précision globale supérieure de 6.67%. Il est conclu que la RF est plus pratique pour évaluer le risque d’irruption d’eau que PNN. La méthode présentée sera utile pour éviter l’appel d’eau et peut également être étendue à diverses applications d’ingénierie.

Resumen

Incidentes de filtraciones de agua en suelos de carbón representan una gran proporción de los desastres en minas de carbón en el norte de China, y una evaluación precisa de los riesgos es crucial para la producción de carbón. Un modelo novedoso y prometedor para simular los flujos de agua ha sido propuesto basado en el bosque aleatorio (RF por sus siglas en inglés), el cual es un potente algoritmo de aprendizaje de máquinas inteligentes. RF tiene ventajas considerables, incluida su alta precisión de clasificación y la capacidad para evaluar la importancia de las variables; en particular, es robusto en lidiar con los problemas complejos y no lineales inherentes en la evaluación de riesgos. En este estudio, el modelo propuesto se aplica a la mina de carbón Panjiayao, en el norte de China. Ocho factores fueron seleccionados como los índices de evaluación de acuerdo a un análisis sistemático de las condiciones geológicas y un estudio de campo de la zona de estudio. Mapas de asesorías de riesgos fueron generados sobre la base de RF, y el modelo de red neuronal probabilística (PNN por sus siglas en inglés) también fue utilizado para la evaluación de riesgos como punto de comparación. Los resultados demuestran que los dos métodos son consecuentes con evaluaciones de riesgo de filtraciones de agua en la mina, y RF muestra un mejor rendimiento en comparación con PNN con una precisión superior por 6.67%. Se concluye que RF es más eficiente para evaluar el riesgo de filtraciones de agua que PNN. El presente método será útil para evitar filtraciones de agua y también puede extenderse a diferentes aplicaciónes de ingeniería.

摘要

煤矿底板突水事故在中国北方煤矿灾害中占很大比例,准确的风险评估是煤矿安全生产的关键。本文提出了一种基于随机森林的突水评价模型,该模型是一种功能强大的智能机器学习算法。随机算法具有很高的分类精度和评估变量重要性的能力,特别是在处理复杂和非线性的问题时具有很强的鲁棒性。本研究将该模型应用于我国北方潘家窑煤矿。根据对地质条件的系统分析和对研究区的实地调查,选取了8个主控因素作为评价指标。基于随机森林模型生成了危险性评价图,并采用概率神经网络模型进行对比。结果表明,这两种方法在矿井突水危险性评价中是基本一致的,随机森林模型与概率神经网络模型相比具有更好的性能,总体精度提高了6.67%。在评价突水危险中,随机森林模型比概率神经网络模型更有效。本文提出的方法既有利于矿井突水的防治,也可以推广到各种工程应用中。

Resumo

Incidentes de entrada de água em piso de carvão são responsáveis por uma grande proporção de desastres em minas de carvão no norte da China, e a avaliação precisa dos riscos é crucial para a produção segura de carvão. Um modelo de avaliação inovador e promissor para o inrush da água é proposto com base na floresta aleatória (RF), que é um poderoso algoritmo inteligente de aprendizado de máquina. RF tem vantagens consideráveis, incluindo alta precisão de classificação e capacidade de avaliar a importância das variáveis; em particular, é robusto ao lidar com os problemas complicados e não lineares inerentes à avaliação de risco. Neste estudo, o modelo proposto é aplicado à Mina de Carvão Panjiayao, norte da China. Oito fatores foram selecionados como índices de avaliação de acordo com a análise sistemática das condições geológicas e um levantamento de campo da área de estudo. Mapas de avaliação de risco foram gerados com base em RF, e o modelo de rede neural probabilística (PNN) também foi utilizado para a avaliação de risco como comparação. Os resultados demonstram que os dois métodos são consistentes na avaliação de risco de inrush de água na mina, e RF mostra um melhor desempenho comparado ao PNN com uma precisão geral maior de 6.67%. Conclui-se que a RF é mais praticável para avaliar o risco de entrada de água do que o PNN. O método apresentado será útil para evitar o inrush da água e também pode ser estendido para várias aplicações de engenharia.

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Acknowledgements

The authors would like to thank the editor and reviewers for their constructive suggestions.

Funding

This research was financially supported by the China National Natural Science Foundation (Grant Nos. 41702261, 41572222, 41602262 and 41430318), the China National Scientific and Technical Support Program (Grant No. 2016YFC0801800), the Beijing Natural Science Foundation (8162036), Fundamental Research Funds for the Central Universities (2010YD02), the Innovation Research Team Program of Ministry of Education (IRT1085) and State Key Laboratory of Coal Resources and Safe Mining.

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Authors and Affiliations

  1. China University of Mining & Technology (Beijing), Beijing, 100083, China

    Dekang Zhao, Qiang Wu, Fangpeng Cui, Yifan Zeng & Yuanze Du

  2. National Engineering Research Center of Coal Mine Water Hazard Controlling, Beijing, 100083, China

    Dekang Zhao, Qiang Wu, Fangpeng Cui, Yifan Zeng & Yuanze Du

  3. Information Engineering College, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

    Hua Xu

  4. Beijing Urban Construction Exploration and Surveying Design Research Institute Co. Ltd., Beijing, 100101, China

    Yufei Cao

Authors
  1. Dekang Zhao

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  2. Qiang Wu

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  3. Fangpeng Cui

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  4. Hua Xu

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  5. Yifan Zeng

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  6. Yufei Cao

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  7. Yuanze Du

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Correspondence toQiang Wu.

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Zhao, D., Wu, Q., Cui, F.et al. Using random forest for the risk assessment of coal-floor water inrush in Panjiayao Coal Mine, northern China.Hydrogeol J26, 2327–2340 (2018). https://doi.org/10.1007/s10040-018-1767-5

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