Institute of Resources and Environment, Henan Polytechnic University, Jiaozuo, 454000, China.
Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization, Jiaozuo, 454000, China.
Sci Rep. 2022 Nov 15;12(1):19568. doi: 10.1038/s41598-022-24037-4.
Machine learning is one of the widely used techniques to pattern recognition. Use of the machine learning tools is becoming a more accessible approach for predictive model development in preventing engineering disaster. The objective of the research is to for estimation of water source using the machine learning tools. Random forest classification is a popular machine learning method for developing prediction models in many research settings. The type of mine water in the Pingdingshan coalfield is classified into surface water, Quaternary pore water, Carboniferous limestone karst water, Permian sandstone water, and Cambrian limestone karst water. Each type of water is encoded with the number 0-4. On the basis of hydrochemical data processing, a random forests model is designed and trained with the hydrochemical data. With respect to the predictive accuracy and robustness, fourfold cross-validation (CV) is adopted for the model training. The results show that the random forests model presented here provides significant guidance for the discrimination of mine water.
机器学习是模式识别中广泛使用的技术之一。使用机器学习工具对于开发预防工程灾害的预测模型来说，正成为一种更易获取的方法。本研究的目的是利用机器学习工具来估算水源。随机森林分类是一种在许多研究环境中开发预测模型的流行机器学习方法。平顶山煤田的矿井水类型分为地表水、第四纪孔隙水、石炭系灰岩岩溶水、二叠系砂岩水和寒武系灰岩岩溶水。每种水类型都用数字 0-4 编码。在水化学数据处理的基础上，利用水化学数据设计并训练了一个随机森林模型。就预测准确性和稳健性而言，采用了四重交叉验证 (CV) 来进行模型训练。结果表明，这里提出的随机森林模型为矿井水的判别提供了重要的指导。
