Optimizing Habitat Prediction for Calotropis procera L. Using Artificial Neural Networks and Multiple Linear Regression Models

Optimizing Habitat Prediction for Calotropis procera L. Using Artificial Neural Networks and Multiple Linear Regression Models | 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 Optimizing Habitat Prediction for Calotropis procera L. Using Artificial Neural Networks and Multiple Linear Regression Models Mansour Taghvaei, Mohammad Amin Nematollahi, Sadgeghiyan Tahereh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6212196/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 Calotropis procera L. is a medicinal plant highly valuable for restoring arid lands. Basic soil attributes and topographic parameters are often readily available in databanks. This study aimed to predict several traits of C. procera , including plant density, crown area, collar circumference, branch number, tree height, and beech regeneration, using basic soil attributes (e.g., soil organic matter, electrical conductivity, pH, water-soluble elements, textural components, and water content) and topographic parameters (e.g., slope and elevation above sea level). Predictions were made using multiple linear regression (MLR), multilayer perceptron neural networks (MLPNNs), and radial basis function neural networks (RBFNNs). Data were collected from 120 locations in the arid pastures of southern Iran. The results showed that MLPNNs performed exceptionally well in predicting the studied traits, obtaining a very good validation dataset coefficient of determination. RBFNNs demonstrated good to very good prediction accuracy. In contrast, MLR exhibited poor to good predictive capability. Overall, the predictive performance of the models ranked as follows: MLPNNs > RBFNNs > MLR. We recommend the use and further development of MLPNN-based models for predicting optimal plant locations based on basic soil and topographic parameters due to their high predictive power. Habitat Modeling Calotropis procera Artificial Neural Networks Soil Parameters Arid Land Restoration Plant Traits Prediction Full Text Additional Declarations No competing interests reported. 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-6212196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436020927,"identity":"c5f5b3c4-6eb1-4a24-bb90-4ad885a61b2a","order_by":0,"name":"Mansour Taghvaei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie2RsWrDMBCGb5KXA60KpO8gEGQKyatIGOzFdE2HYNTFY7zmYTLIGNzFpVvxGBPw5MVL6OREHhrIorRbBn3Dge708Z8QgMfznJCpIAUEc+tJx338VWb6vwpwg3/cah18fJ9wu5yLr8+ifNumSgflEdqDIwWTjcAqwkXzKou6IkpjxEF2rsWSaLYnpVWQF5qg0pDYtxiHQnurjBcUeW2VkSlN+wcKiys2ZAY5JLx4z7jS7FFK0xM27EJkzaTspMhYx41LCfK4Y/K8WtO8FoM+py85Ddv2x6HYHH5/nr7JKdico3vu8Xg8nitCP1ANd+cSDwAAAABJRU5ErkJggg==","orcid":"","institution":"Shiraz University","correspondingAuthor":true,"prefix":"","firstName":"Mansour","middleName":"","lastName":"Taghvaei","suffix":""},{"id":436020928,"identity":"5697821a-da48-4e9b-a7ee-088a5c50e578","order_by":1,"name":"Mohammad Amin Nematollahi","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amin","lastName":"Nematollahi","suffix":""},{"id":436020929,"identity":"e0850ceb-5140-4096-a173-49ba2677ec70","order_by":2,"name":"Sadgeghiyan Tahereh","email":"","orcid":"","institution":"Shiraz University","correspondingAuthor":false,"prefix":"","firstName":"Sadgeghiyan","middleName":"","lastName":"Tahereh","suffix":""},{"id":436020930,"identity":"c6629604-a600-4c73-ab7e-1347cfcb7f20","order_by":3,"name":"Vlad Sebastian Popescu","email":"","orcid":"","institution":"University of Brescia","correspondingAuthor":false,"prefix":"","firstName":"Vlad","middleName":"Sebastian","lastName":"Popescu","suffix":""}],"badges":[],"createdAt":"2025-03-12 12:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6212196/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6212196/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81658085,"identity":"f9445528-3fcc-4ede-8bf2-8584d3d5d26d","added_by":"auto","created_at":"2025-04-29 19:31:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":689601,"visible":true,"origin":"","legend":"","description":"","filename":"1Calotropispaperfulltext.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6212196/v1_covered_90ed4b1e-694e-4f66-bee7-06e21efffdeb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Habitat Prediction for Calotropis procera L. 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Basic soil attributes and topographic parameters are often readily available in databanks. This study aimed to predict several traits of \u003cem\u003eC. procera\u003c/em\u003e, including plant density, crown area, collar circumference, branch number, tree height, and beech regeneration, using basic soil attributes (e.g., soil organic matter, electrical conductivity, pH, water-soluble elements, textural components, and water content) and topographic parameters (e.g., slope and elevation above sea level). Predictions were made using multiple linear regression (MLR), multilayer perceptron neural networks (MLPNNs), and radial basis function neural networks (RBFNNs). Data were collected from 120 locations in the arid pastures of southern Iran. The results showed that MLPNNs performed exceptionally well in predicting the studied traits, obtaining a very good validation dataset coefficient of determination. RBFNNs demonstrated good to very good prediction accuracy. 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