[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121557-en":3,"doc-seo-121557-105":30,"detail-sidebar-cat-0-en-105":91},{"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},121557,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Construction of a predictive model of shovel productivity applying machine learning algorithms - Research","Mining operations require higher efficiency while reducing costs and optimizing equipment and human resources under increasing competitiveness. Accumulated production data are typically stored in statistical reports but often remain underused for identifying less efficient technological process segments. This work builds a predictive “shovel” productivity model using machine learning with acceptable confidence, integrating quantitative and qualitative variables to improve accuracy beyond traditional approaches.","Document downloaded from the institutional repository of the University of  \nAlcala: http://ebuah.uah.es/dspace/  \nThis isa posprint version of the following published document:  \nJuarez Racchumi, V., Rosales Huamani, J.A. & Castillo Sequera, J.L. 2025,“Construction of a predictive model of shovel productivity applying machinelearning algorithms”, Earth Science Informatics, vol. 18, art. no. 86, pp. 1-17.  \nAvailable at https://dx.doi.org/10.1007/s12145-024-01563-5  \n© 2024 The authors, under exclusive licence to Springer-Verlag GmbHGermany, part of Springer Nature  \n(Article begins on next page)  \nEarth Science Informatics _ \\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#\\#_https://doi.org/10.1007/s12145-024-01563-5  \nRESEARCH  \n# Construction of a predictive model of shovel productivity applying\n\nmachine learning algorithms  \nVictor Juarez Racchumi1 · Jimmy Aurelio Rosales Huamani1,2 · Jose Luis Castillo Sequera2  \nReceived: 22 June 2024 / Accepted: 18 October 2024  \n© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024  \n# 1 Abstract\n\n1 2 Currently, many mining companies face the need to increase the efficiency of their performance in conditions of growing  \n3 competitiveness and market globalization. To achieve the set objectives, itis essential to reduce costs, optimize the use of  \n4 equipment and human resources. Mining companies usually implement technological process control systems where a large  \n5 amount of updated information about their production processes is stored, normally using long-term accumulated information  \n6 that consists mainly of statistical reports. Taking the above into account, the need arises to use this accumulated information  \n7 to identify segments of technological processes that are less efficient and be able to improve productivity. To do this, new  \n8 technologies can be used such as the use of Machine Learning (ML) . The present work focuses on the creation of a predictive  \n9 model of“shovel”productivity, where shovels are heavy load equipment, used for any activity that involves moving earth or  \n10 rocks in large volumes, with a levelof acceptable confidence, which unlike the traditional method, include other quantitative  \n11 and qualitative variables. The research began with the general hypothesis that the productivity model developed could improve  \n12 the prediction of blade productivity with greater accuracy using Machine Learning models. From the results we obtained that  \n13 the model that best predicts the productivity of the blades is the “Random Forest Regressor” with an accuracy of 99.7% for  \n14 the training and 91.1% forthe testing DataSet; while the worst Machine Learning precision model was “Dummy Regressor”.  \n15 Keywords Machine learning · Random forest · Productivity · Training · Shovel  \n\n| Communicated by: Hassan Babaie   |\n| --- |\n| Victor Juarez Racchumi, Jimmy Aurelio Rosales Huamani and JoseLuis Castillo Sequera contributed equally to this work   |\n\nB Jimmy Aurelio Rosales Huamanijrosales@uni.edu.pe  \nVictor Juarez Racchumi  \nvictor.juarez.r@uni.pe  \nJose Luis Castillo Sequera  \njluis.castillo@uah.es  \n1 Multidisciplinary Sensing, Universal Accessibilityand Machine Learning Group, National Universityof Engineering, Tupac Amaru Avenue-210, Rimac,Lima 15333, Peru  \n2 Department of Computer Science, Higher PolytechnicSchool, Universidad deAlcala, Campus Universitario.Ctra. Madrid-Barcelona, Km. 33,600, Alcala de Henares,Madrid 28805, Spain  \n# Introduction 16\n\nCurrently, companies are looking for a way to optimize  \n17  \nresources, generate value in the business through greater pro-  \n18  \nductivity of their operation teams. In the case of the mining  \n19  \nsector, itis no exception since each of its activities are criti-  \n20  \ncal andthe impact that maybe generated in any of them will  \n21  \nsignificantly affect the company (Zelinska 2020) . 22In mining, several of the activities with the greatest eco-  \n23  \nnomic impact ar","cbCaiqneeFaxolnD","https://ap.wps.com/l/cbCaiqneeFaxolnD","pdf",2503330,1,18,"English","en",105,"# Abstract\n# Introduction\n## Mining data and productivity optimization\n## Machine learning in mining operations","[{\"question\":\"Why do mining companies need predictive models for shovel productivity?\",\"answer\":\"To increase operational efficiency in competitive market conditions while reducing costs and optimizing the use of equipment and human resources.\"},{\"question\":\"What data source does the study rely on for building the productivity model?\",\"answer\":\"Accumulated information stored in long-term technological process control systems, often in statistical reports, including quantitative and qualitative variables.\"},{\"question\":\"Which machine learning model performed best in the study results?\",\"answer\":\"The Random Forest Regressor best predicted blade (shovel-related) productivity, achieving 99.7% accuracy on training and 91.1% on the testing dataset, while the Dummy Regressor was the worst.\"}]","Construction of a predictive model of shovel productivity applying machine learning algorithms - 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