Development of a model based on Support Vector Machines to predict the degradation of pesticides in biobeds systems

Development of a model based on Support Vector Machines to predict the degradation of pesticides in biobeds systems | 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 Development of a model based on Support Vector Machines to predict the degradation of pesticides in biobeds systems Ahreel Molina-Chuc, César Arturo Aceves-Lara, Marisela Vega De Lille, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3662137/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 Pesticides are chemical compounds used to mitigate, reduce, or eliminate the impact of pests on agricultural production. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides in soils, the use of biological beds (biobeds) is a feasible option to minimize their contamination. The main problematic to use biobeds is the difficult to predict their behavior due biotic and abiotic factors. This study focuses on the use of the support vector machine (SVM), for the generation of predictive models of pesticide degradation in biobeds systems. The results show that the Gaussian and polynomial kernel has the best performance to model experimental data. The statistical parameters of R-Squared were 0.93 for Gaussian kernel and polynomial, 0.83 for cubic, 0.76 for quadratic and 0.52 for lineal. The Gaussian model could be used to provide the characteristics to improve of pesticide degradation. Biobeds Pesticide waste treatment Support vector regression. Optimization. Full Text 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3662137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265753747,"identity":"19254c37-03b4-48e9-aa29-4058d206d143","order_by":0,"name":"Ahreel Molina-Chuc","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCRBRAMTsPWA+Dx9xWgxAas8wMBwAUmzEa5HIAWthIKiFf3bvwwcfDBii+We+Pfj4Y46dDBsD88NHN/BZcue4seEMA4bcGbfzkg0ObksGOozN2DgHnzU30tikeQz+526QzjGTOLiNGaiFh00anxb5G2nsv/8AbdkgeQakpZ6wFgOgLcwMIC0SPCAthwlrMbyRxizZA/LLmRxjg7PbjvOwMRPwi9yNNMYPPyoYcvvbzxg+qNxWbc/P3vzwMV7vYwJm0pSPglEwCkbBKMACAK49QoX3OkcdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-2114-7607","institution":"Universidad Autonoma de Yucatan Facultad de Ingenieria","correspondingAuthor":true,"prefix":"","firstName":"Ahreel","middleName":"","lastName":"Molina-Chuc","suffix":""},{"id":265753748,"identity":"d7947b37-3e3d-4454-b559-711c27e814e9","order_by":1,"name":"César Arturo Aceves-Lara","email":"","orcid":"","institution":"TBI: Toulouse Biotechnology Institute","correspondingAuthor":false,"prefix":"","firstName":"César","middleName":"Arturo","lastName":"Aceves-Lara","suffix":""},{"id":265753749,"identity":"88370f3d-ca9b-48ef-9774-ad98c8357b9f","order_by":2,"name":"Marisela Vega De Lille","email":"","orcid":"","institution":"Universidad Autonoma de Yucatan Facultad de Ingenieria","correspondingAuthor":false,"prefix":"","firstName":"Marisela","middleName":"Vega","lastName":"De Lille","suffix":""},{"id":265753750,"identity":"6a5e415e-c5ec-4840-b57d-21b10ddae9b9","order_by":3,"name":"Carlos Quintal-Franco","email":"","orcid":"","institution":"Universidad Autonoma de Yucatan Facultad de Ingenieria","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Quintal-Franco","suffix":""},{"id":265753751,"identity":"0c852c6e-c813-47b8-9f88-52f3a6f12078","order_by":4,"name":"Carmen Ponce-Caballero","email":"","orcid":"","institution":"Universidad Autonoma de Yucatan Facultad de Ingenieria","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Ponce-Caballero","suffix":""}],"badges":[],"createdAt":"2023-11-25 05:25:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3662137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3662137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50475197,"identity":"5aa393e8-de82-42f7-b86a-56b4b59aa7d4","added_by":"auto","created_at":"2024-02-01 05:43:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":418680,"visible":true,"origin":"","legend":"","description":"","filename":"DevelopmentamodelbasedonSVM.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3662137/v1_covered_325bae1e-c885-453a-a9a9-3e2c7ce23fa8.pdf"}],"financialInterests":"","formattedTitle":"Development of a model based on Support Vector Machines to predict the degradation of pesticides in biobeds systems","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biobeds, Pesticide waste treatment, Support vector regression. 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