[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121633-en":3,"doc-seo-121633-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121633,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Comparative Analysis for Slope Stability by Using Machine Learning Methods","Slope stability analysis is a core geotechnical task that informs suitable stabilization measures for rock and soil slopes. Safety factor (F.S) calculation is essential for reliable assessment and operational success, motivating accurate predictive approaches. This study compares computational intelligence and machine learning methods to estimate F.S using MLP, SVM, DT, and RF trained on a dataset of 100 Iranian earth slope cases. Models are validated by Janbu’s limit equilibrium analysis and GeoStudio, with MLP achieving the best performance and lowest average loss.","applied sciences  \nArticle  \nComparative Analysis for Slope Stability by Using Machine Learning Methods  \nYaser A. Nanehkaran 1, Zhu Licai 1, Jin Chengyong 2, Junde Chen 3, Sheraz Anwar 4, Mohammad Azarafza 5 and Reza Derakhshani 6, *  \nCitation: Nanehkaran, Y.A.; Licai, Z.; Chengyong, J.; Chen, J.; Anwar, S.; Azarafza, M.; Derakhshani, R. Comparative Analysis for Slope Stability by Using Machine Learning Methods. Appl. Sci. 2023, 13, 1555 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)app13031555  \nAcademic Editor: Roohollah Kalatehjari  \nReceived: 5 December 2022  \nRevised: 1 January 2023  \nAccepted: 10 January 2023  \nPublished: 25 January 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 School of Information Engineering, Yancheng Teachers University, Yancheng 224002, China  \n2 Academy of Engineering and Technology, Yang-En University, Quanzhou 362014, China  \n3 Department of Electronic Commerce, Xiangtan University, Xiangtan 411105, China  \n4 School of Informatics, Xiamen University, Xiamen 361005, China  \n5 Department of Civil Engineering, University of Tabriz, Tabriz 5166616471, Iran  \n6 Department of Earth Sciences, Utrecht University, 3584 CB Utrecht, The Netherlands  \n* Correspondence: [r.derakhshani@uu.nl](r.derakhshani@uu.nl)  \nFeatured Application: The presented paper conducted a comparative analysis based on well-known MLP, SVM, DT, and RF learning methods to assess/predict the safety factor (F.S) of earthslopes.  \nAbstract: Earth slopes' stability analysis is a key task in geotechnical engineering that provides a detailed view of the slope conditions used to implement appropriate stabilizations. In the stability analysis process, calculating the safety factor (F.S) plays an essential part in the stability assessment, which guarantees operations' success. Providing accurate and reliable F.S can be used to improve the stability analysis procedure as well as stabilizations. In this regard, researchers used computational intelligent methodologies to reach highly accurate F.S calculations. The presented study focused on the F.S estimation process and attempted to provide a comparative analysis based on computational intelligence and machine learning methods. In this regard, the well-known multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF) learning algorithms were used to predict/calculate F.S for the earth slopes. These machine learning classifiers have a strong capability predict the F.S under certain conditions for slope failures and uncertainties. These models were implemented on a dataset containing 100 earth slopes' stabilities, recorded based on F.S from various locations in the provinces of Fars, Isfahan, and Tehran in Iran, which were randomly divided into the training and testing datasets. These predictive models were validated by Janbu's limit equilibrium analysis method (LEM) and GeoStudio commercial software. Regarding the study's results, MLP (accuracy = 0.901/precision = 0.90) provides more accurate results to predict the F.S than other classifiers, with good agreement with LEM results. The SVM algorithm follows MLP (accuracy = 0.873/precision = 0.85) . Regarding the estimated loss function, MLP obtained a 0.29 average loss in the F.S prediction process, which is the lowest rate. The SVM, DT, and RF obtained 0.41, 0.62, and 0.45 losses, respectively. This article tried to fill the gap in traditional analysis procedures based on advanced procedures in slope stability assessments.  \nKeywords: machine learning; slope stability; predictive models; limit equilibrium analysis; factor of safety  \n1. Introduction  \n","cbCailVvPLsYHaCR","https://ap.wps.com/l/cbCailVvPLsYHaCR","pdf",2497737,1,14,"English","en",105,"# Introduction\n## Slope stability and failure factors\n## Data-driven estimation of safety factor (F.S)","[{\"question\":\"What is the main goal of the study on slope stability?\",\"answer\":\"To estimate or predict the safety factor (F.S) of earth slopes by comparing several machine learning approaches for slope failure assessment.\"},{\"question\":\"Which machine learning algorithms are compared in the paper?\",\"answer\":\"The study uses multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF).\"},{\"question\":\"How are the predictive models validated?\",\"answer\":\"Predictions are validated using Janbu’s limit equilibrium method (LEM) and the GeoStudio commercial software, and compared against agreement with LEM results.\"}]","Comparative Analysis for Slope Stability by Using Machine Learning Methods | PDF",1785805852,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"comparative-analysis-for-slope-stability-by-using-machine-learning-methods","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/comparative-analysis-for-slope-stability-by-using-machine-learning-methods/121633/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the study on slope stability?","Question",{"text":76,"@type":77},"To estimate or predict the safety factor (F.S) of earth slopes by comparing several machine learning approaches for slope failure assessment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared in the paper?",{"text":81,"@type":77},"The study uses multilayer perceptron (MLP), decision tree (DT), support vector machines (SVM), and random forest (RF).",{"name":83,"@type":74,"acceptedAnswer":84},"How are the predictive models validated?",{"text":85,"@type":77},"Predictions are validated using Janbu’s limit equilibrium method (LEM) and the GeoStudio commercial software, and compared against agreement with LEM results.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]