{"paper_id":"09ac96ae-3bc5-4e61-a3b8-dfaed65e915f","body_text":"Sustainability evaluation of basin indicator: A bibliometric analysis using Latent Dirichlet Allocation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sustainability evaluation of basin indicator: A bibliometric analysis using Latent Dirichlet Allocation Rebeca Moreno-Santoyo, Andres Garcia-Medina, Maria Concepcion Arredondo Garcia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6575497/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the evolution of scientific production related to the assessment of basin sustainability by applying the Latent Dirichlet Allocation~(LDA) model to the content analysis of 2,675 scientific publications. Since 1992, following the United Nations Conference on Environment and Development, the basin has been recognized as the ideal geographic unit for the sustainable management and planning of natural resources. Within this framework, the importance of monitoring basin components has been emphasized in order to evaluate the effectiveness of implemented actions and monitor progress toward sustainable development. The incorporation of tools from Natural Language Processing~(NLP) and Machine Learning~(ML) enabled the identification of the main research topics, as well as thematic and methodological shifts over time. These findings are particularly relevant in the context of the Sustainable Development Goals~(SDGs), as they provide a comprehensive overview of the state of scientific knowledge and its potential contribution to achieving global targets by 2030. Terrestrial Ecology Environmental Policy Bibliometric Analysis Latent Dirichlet Allocation(LDA) Basin Sustainability Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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