Determination of the stress concentration factor adjacent an extracted underground coal panel using the CART and MARS algorithms

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-09-09 DOI:10.1007/s12145-024-01476-3
Mohammad Rezaei, Hazhar Habibi, Mostafa Asadizadeh
{"title":"Determination of the stress concentration factor adjacent an extracted underground coal panel using the CART and MARS algorithms","authors":"Mohammad Rezaei, Hazhar Habibi, Mostafa Asadizadeh","doi":"10.1007/s12145-024-01476-3","DOIUrl":null,"url":null,"abstract":"<p>In this study, classification and regression tree (CART) and multivariate adaptive regression spline (MARS) models are proposed to predict the stress concentration factor (SCF) around an extracted underground coal panel. Models are trained and tested using 120 collected datasets with 100 series allocated for models training and 20 datasets reserved for testing. For SCF prediction using the CART and MARS models, input parameters including overburden thickness (H), specific gravity of rock mass (γ), straight distance from the panel edge (D), and height of disturbed zone over the mined panel (H<sub>d</sub>) are utilized, employing principal component analysis (PCA) to remove correlations. A predictive tree graph and 17 if–then rules with quantitative outputs are generated from the CART model, while a predictive equation is derived from the MARS technique for SCF prediction. The achieved values of the coefficient of determination (R<sup>2</sup>) for CART and MARS models are 0.940 and 0.957, respectively. Furthermore, obtained amounts of normalized root mean square error (NRMSE), variant account for (VAF), and performance index (PI) for CART are 0.043 92.473%, and 1.82, respectively. For the MARS model these values are 0.035, 95.419%, and 1.876,. Additionally, performance evaluations of the models using the Wilcoxon Signed Ranks and Friedman non-parametric tests, along with Taylor diagrams and error analysis demonstrate the reliability and suitability of the proposed models for SCF prediction. However, error and accuracy analyses confirm that MARS model yields more precise outputs, achieving 2.57% greater accuracy and 10.84% lower error than the CART model. Furthermore, the importance analysis demonstrated that both H and H<sub>d</sub> have the highest importance on the SCF, while γ has the lowest, with importance values of 33.33% and 11.11%, respectively. Models verification based on the field SCF measurement confirms the models validity, as indicated by the relative errors of 6.83 for the MARS model and 7.05 for the CART model. Finally, a comparative analysis based on a case study data validates the practical application of the proposed models.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"74 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01476-3","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

In this study, classification and regression tree (CART) and multivariate adaptive regression spline (MARS) models are proposed to predict the stress concentration factor (SCF) around an extracted underground coal panel. Models are trained and tested using 120 collected datasets with 100 series allocated for models training and 20 datasets reserved for testing. For SCF prediction using the CART and MARS models, input parameters including overburden thickness (H), specific gravity of rock mass (γ), straight distance from the panel edge (D), and height of disturbed zone over the mined panel (Hd) are utilized, employing principal component analysis (PCA) to remove correlations. A predictive tree graph and 17 if–then rules with quantitative outputs are generated from the CART model, while a predictive equation is derived from the MARS technique for SCF prediction. The achieved values of the coefficient of determination (R2) for CART and MARS models are 0.940 and 0.957, respectively. Furthermore, obtained amounts of normalized root mean square error (NRMSE), variant account for (VAF), and performance index (PI) for CART are 0.043 92.473%, and 1.82, respectively. For the MARS model these values are 0.035, 95.419%, and 1.876,. Additionally, performance evaluations of the models using the Wilcoxon Signed Ranks and Friedman non-parametric tests, along with Taylor diagrams and error analysis demonstrate the reliability and suitability of the proposed models for SCF prediction. However, error and accuracy analyses confirm that MARS model yields more precise outputs, achieving 2.57% greater accuracy and 10.84% lower error than the CART model. Furthermore, the importance analysis demonstrated that both H and Hd have the highest importance on the SCF, while γ has the lowest, with importance values of 33.33% and 11.11%, respectively. Models verification based on the field SCF measurement confirms the models validity, as indicated by the relative errors of 6.83 for the MARS model and 7.05 for the CART model. Finally, a comparative analysis based on a case study data validates the practical application of the proposed models.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用 CART 和 MARS 算法确定井下采掘煤板附近的应力集中系数
本研究提出了分类回归树(CART)和多元自适应回归样条线(MARS)模型,用于预测井下采掘煤板周围的应力集中系数(SCF)。使用收集的 120 个数据集对模型进行了训练和测试,其中 100 个数据集用于模型训练,20 个数据集用于测试。使用 CART 和 MARS 模型预测 SCF 时,输入参数包括覆盖层厚度 (H)、岩体比重 (γ)、距采煤层边缘的直线距离 (D) 和采煤层上扰动区的高度 (Hd),并采用主成分分析 (PCA) 消除相关性。CART 模型生成了一个预测树状图和 17 个具有定量输出的 "如果-那么 "规则,而用于 SCF 预测的 MARS 技术则导出了一个预测方程。CART 模型和 MARS 模型的判定系数 (R2) 分别为 0.940 和 0.957。此外,CART 的归一化均方根误差 (NRMSE)、变异系数 (VAF) 和性能指数 (PI) 分别为 0.043 92.473% 和 1.82。对于 MARS 模型,这些值分别为 0.035、95.419% 和 1.876。此外,利用 Wilcoxon Signed Ranks 和 Friedman 非参数检验对模型进行的性能评估,以及泰勒图和误差分析表明了所提出的模型在 SCF 预测中的可靠性和适用性。然而,误差和准确度分析证实,MARS 模型能产生更精确的输出结果,其准确度比 CART 模型高 2.57%,误差低 10.84%。此外,重要性分析表明,H 和 Hd 对 SCF 的重要性最高,而 γ 的重要性最低,重要性值分别为 33.33% 和 11.11%。基于现场 SCF 测量的模型验证证实了模型的有效性,MARS 模型的相对误差为 6.83,CART 模型的相对误差为 7.05。最后,基于案例研究数据的对比分析验证了所提模型的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
期刊最新文献
Estimation of the elastic modulus of basaltic rocks using machine learning methods Feature-adaptive FPN with multiscale context integration for underwater object detection Autoregressive modelling of tropospheric radio refractivity over selected locations in tropical Nigeria using artificial neural network Time series land subsidence monitoring and prediction based on SBAS-InSAR and GeoTemporal transformer model Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1