首页|期刊简介|投稿指南|分类索引|刊文选读|订阅指南|资料|样刊邮寄查询|常见问题解答|联系我们
郝谦,武雄,穆文平,等. 基于随机森林模型判别矿井涌(突)水水源[J]. 科学技术与工程, 2020, 20(16): 6411-6418.
haoqian,mu wenping,et al.Based on Random Forest Model Identify Mine Gushing Water Source[J].Science Technology and Engineering,2020,20(16):6411-6418.
基于随机森林模型判别矿井涌(突)水水源
Based on Random Forest Model Identify Mine Gushing Water Source
投稿时间:2019-09-16  修订日期:2020-06-14
DOI:
中文关键词:  矿井涌(突)水水源判别  化学成分  随机森林
英文关键词:discrimination of mine gushing water source  hydrochemical composition  random forest
基金项目:国家自然科学基金资助项目 项目名称:人类工程活动对红碱淖流域水文循环及湖泊面积的影响研究(41572227);国家重点研发计划资助项目 项目名称:煤-水协调开发新模式与水资源高效利用关键技术(2018YFC0406404)
                 
作者单位
郝谦 中国地质大学北京 水资源与环境学院
武雄 中国地质大学北京 水资源与环境学院
穆文平 中国地质大学北京 水资源与环境学院
邓若晨 中国地质大学北京 水资源与环境学院
胡博远 中国地质大学北京 水资源与环境学院
高原 中国地质大学北京 水资源与环境学院
摘要点击次数: 89
全文下载次数: 36
中文摘要:
      快速准确判别矿井涌(突)水水源对保障矿井安全生产有重要意义。近年来人类的活动对不同含水层的影响与日俱增,为提高矿井涌(突)水水源判别准确性,文章提出选取地下水中7种常见离子浓度,和能够充分反映人类活动痕迹的硝酸根离子浓度及化学需氧量作为水化学判别指标,后采用随机森林模型进行矿井涌(突)水水源判别。为验证选取指标和判别方法的有效性,文章以大孤山铁矿为例,将数据输入随机森林模型进行100次交叉验证,并将验证结果与支持向量机模型和极限学习机模型进行比较。结果表明,随机森林模型预测结果稳定性较强,预测正确率不容易波动;随机森林在建模过程中参数拥有宽广的适应范围。树的棵数为50时,训练误差趋于稳定,改变树的棵数对预测结果没有实际影响,而其余二者对参数选取较为敏感;随机森林的参数可以通过OOB错误率简单的得到,而其余二者参数调整时需要通过交叉验证的方式才可以取得;随机森林对训练样本进行验证,正确率可达100%,对测试样本进行验证,正确率可达97.38%,两项精度均优于支持向量机与极限学习机;随机森林模型拥有更高的预测精度和鲁棒性,在矿井涌(突)水水源判定方面有较好的应用前景。
英文摘要:
      Quick and accurate detection of mine water source is of great importance for the safety of mine production. In recent years, the impact of human activities on different aquifers has increased. In order to improve the accuracy of water source in mine water, this paper propose to select seven common ion concentrations in groundwater, and the nitrate ion concentration as well as chemical oxygen demand which can fully reflect the traces of human activity as the water chemical discriminant index. And then the RF model would be used to identify the mine water source. Taking the Dagushan iron ore as an example to verify the validity of the selected indicators and methods. The data was input into the RF model for 100 cross-validation, and the verification results were compared with the SVM model and the ELM model. The results of the RF model are stable and the prediction accuracy is not susceptible to fluctuation. RF have a wide range of adaptation parameters in the modeling process. When the number of trees is 50, the training error tends to be stable, and changing the number of trees has no practical impact on the prediction results, while the other two models are more sensitive to parameter selection. The parameters of the RF can be obtained simply by the OOB error rate, but the other two model parameters need to be obtained through cross-validation. The RF model verifies the training samples, and the correct rate can reach 100%. The RF model also verifies the test samples, and the correct rate can reach 97.38%. The accuracy of the two correct rates is better than the SVM and the ELM; The random forest model has higher prediction accuracy and robustness, and has a better application prospect in the determination of mine water source.
查看全文  查看/发表评论  下载PDF阅读器
关闭
你是第31350837位访问者
版权所有:科学技术与工程编辑部
主管:中国科学技术协会    主办:中国技术经济学会
Tel:(010)62118920 E-mail:stae@vip.163.com
京ICP备05035734号-4
技术支持:本系统由北京勤云科技发展有限公司设计

京公网安备 11010802029091号