Article(id=1281616696620323249, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.02.0434, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1757433600000, receivedDateStr=2025-09-10, revisedDate=1765296000000, revisedDateStr=2025-12-10, acceptedDate=null, acceptedDateStr=null, onlineDate=1783490831761, onlineDateStr=2026-07-08, pubDate=1772208000000, pubDateStr=2026-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783490831761, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783490831761, creator=13701087609, updateTime=1783490831761, updator=13701087609, issue=Issue{id=1281616655499366465, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='2', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1772208000000', pubDateStr='2026-02-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783490821957, creator='13701087609', updateTime=1783588693138, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027157069218127, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027157069218128, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281616655499366465, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=163, endPage=171, ext={EN=ArticleExt(id=1281616696851009970, articleId=1281616696620323249, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Prediction model of blasting fragmentation based on GA-QLightGBM quantile regression, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

To address the challenges of high uncertainty and complex influencing factors in predicting blast fragmentation in mining operations, this study proposed a LightGBM prediction model GA-QLightGBM that integrated GA optimisation with quantile regression. First, GA was employed to optimise the hyperparameters of LightGBM by simulating the natural selection process (selection, crossover, and mutation), thereby improving the model's predictive accuracy and stability. Then, different quantiles were set to construct prediction intervals for blast fragmentation, enabling the quantification of prediction uncertainty. Finally, the proposed model was applied to mine field datasets to verify its predictive performance and generalisation ability, providing an effective approach for blast fragmentation prediction and uncertainty analysis. The results show that the model achieves a coefficient of determination (R²) of 0.880 and a mean squared error (MSE) of 0.004 in point prediction, outperforming traditional point prediction models. In interval prediction, the prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and corrected prediction interval accuracy (CPIA) are 0.947, 0.228, and 0.762, respectively, confirming the accuracy and reliability of GA-QLightGBM model. These findings offer a practical framework for quantifying the uncertainty of blast fragmentation, supporting refined blast design and risk control in mining engineering.

, authors=Shuxian WANG¹, Yi YANG^{**, 1}, Yulian SHI², Yaxi SHEN³, authorsList=Shuxian WANG, Yi YANG, Yulian SHI, Yaxi SHEN, authorCompany=null, correspAuthors=Yi YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1281616697303994805, articleId=1281616696620323249, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于GA-QLightGBM分位数回归的爆破块度预测模型, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

针对矿山爆破块度预测中存在的不确定性高、影响因素复杂等问题，提出一种融合遗传算法（GA）优化与分位数回归的轻量级梯度提升机（LightGBM）预测模型（GA-QLightGBM）。首先，利用GA优化LightGBM超参数，通过模拟自然选择过程（选择、交叉、变异）进行寻优，提升模型预测精度与稳定性；然后，通过设置不同分位数构建爆破块度的预测区间，量化预测结果的不确定性；最后，将该模型应用于矿山实测数据集，对比验证其预测性能与泛化能力，为爆破块度预测及不确定性分析提供新思路。结果表明：该模型在点预测方面的决定系数为0.880，均方误差（MSE）为0.004，优于传统点预测模型；在区间预测方面，覆盖概率（PICP）、归一化平均带宽（PINAW）和修正区间预测精度（CPIA）分别为0.947、0.228和0.762，验证了GA-QLightGBM的准确性与可靠性。

, authors=王淑贤¹, 杨溢教授^{**, 1}, 石玉莲², 沈亚玺³, authorsList=王淑贤, 杨溢教授, 石玉莲, 沈亚玺, authorCompany=null, correspAuthors=杨溢教授, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=r7jon6Mwabea70UctJQyFw==, magXml=5D07D+Fn+5SaQ7fcTC3+MA==, pdfUrl=null, pdf=B5aYA6W3XXNZWuGDil6vZg==, pdfFileSize=2935271, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=uw8UqQfo+0VWcVb5Jl79WQ==, mapNumber=null, fund=null)}, authors=[Author(id=1281616697765368256, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616696620323249, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=1766018969@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281616697840865730, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616696620323249, authorId=1281616697765368256, language=EN, stringName=Shuxian WANG, firstName=Shuxian, middleName=null, lastName=WANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming Yunnan 650032, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281616697912168899, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281616696620323249, authorId=1281616697765368256, language=CN, stringName=王淑贤, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹昆明理工大学 公共安全与应急管理学院，云南 昆明 650032, bio={"content":"

王淑贤（2001—），女，云南曲靖人，硕士研究生，主要研究方向为爆破安全。E-mail:1766018969@qq.com。

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王淑贤（2001—），女，云南曲靖人，硕士研究生，主要研究方向为爆破安全。E-mail:1766018969@qq.com。

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