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To improve the prediction accuracy and reliability of slope instability in mine waste dumps, a hybrid GA-BP model was developed by integrating an improved GA with a BP neural network. The model employed GA to globally optimize the initial weights and thresholds of the BP network, and incorporated the Levenberg-Marquardt (LM) algorithm to enhance convergence speed. Ten key parameters—including bench slope angle, geotechnical internal stress, bench height, surface displacement, and pore water pressure—were selected as inputs, with the slope safety factor as the output. Training and validation was performed using 150 field case datasets. The results show that GA-BP model reduces the mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 46.9%, 25.4%, and 5.38%, respectively, compared to the conventional BP model. Predictions are closer to the safety threshold (F_s = 1.2), indicating enhanced sensitivity and stability. Pearson correlation analysis confirms strong relationships between surface and internal displacement (0.98) and between pore water pressure and rainfall (0.75), supporting the rationality of the input indicators. The study demonstrates that GA-BP model effectively overcomes local optima and gradient vanishing issues in BP networks, providing a reliable tool for intelligent slope stability assessment.

, authors=Zunxian XIE^{1, 2}, Haohao MA¹, Song JIANG¹, Xiaoyun WU¹, authorsList=Zunxian XIE, Haohao MA, Song JIANG, Xiaoyun WU, authorCompany=null, correspAuthors=null, 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=1281608152957293097, articleId=1281608150864335399, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于GA-BP神经网络的露天矿山排土场边坡失稳预测, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为提高矿山排土场边坡失稳预测的准确性与可靠性，构建一种基于改进遗传算法（GA）优化反向传播（BP）神经网络的露天矿山排土场边坡失稳预测模型。利用GA全局优化BP神经网络的权值和阈值，并引入Levenberg-Marquardt（LM）算法以提升网络收敛效率；选取台阶坡面角、岩土内应力、台阶高度、地表位移、孔隙水压力等10个关键指标作为输入，以边坡安全系数为输出，并通过150组矿山案例数据进行模型训练与验证。结果表明：相较于传统BP模型，GA-BP模型的均方误差（MSE）、平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别降低46.9%、25.4%和5.38%，预测值更贴近安全系数阈值（F_s=1.2），预测灵敏度和稳定性显著提升。皮尔森相关性分析进一步显示，地表位移与内部位移（0.98）、孔隙水压力与降雨量（0.75）呈强相关性，验证了输入指标的合理性。

, authors=谢尊贤副研究员^{1, 2}, 马浩浩¹, 江松¹, 武潇云¹, authorsList=谢尊贤副研究员, 马浩浩, 江松, 武潇云, authorCompany=null, correspAuthors=null, authorNote=, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=s2uDAynFY9o4ZV0jpYcCwQ==, magXml=QMy1vb/k6CaDJVnvyDGR/w==, pdfUrl=null, pdf=khuP3JENEVD+gpNCd3rLTA==, pdfFileSize=4869617, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=+4NCUnYg9m1Fiw6cevU3ww==, mapNumber=null, fund=null)}, authors=[Author(id=1281608153368334897, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608150864335399, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=xzxie@foxmail.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1281608153460609588, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608150864335399, authorId=1281608153368334897, language=EN, stringName=Zunxian XIE, firstName=Zunxian, middleName=null, lastName=XIE, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China
²Institute of Higher Education, Xi'an University of Architecture and Technology, Xi'an Shaanxi 710055, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608153519329845, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608150864335399, authorId=1281608153368334897, language=CN, stringName=谢尊贤副研究员, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹西安建筑科技大学 资源工程学院，陕西 西安 710055
²西安建筑科技大学 高教研究所，陕西 西安 710055, bio={"content":"

谢尊贤（1966—），男，甘肃平凉人，博士，副研究员，主要从事公共管理、管理科学与工程、安全科学与工程等方面的研究。E-mail:xzxie@foxmail.com。

"}, bioImg=null, bioContent=

谢尊贤（1966—），男，甘肃平凉人，博士，副研究员，主要从事公共管理、管理科学与工程、安全科学与工程等方面的研究。E-mail:xzxie@foxmail.com。

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