The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index

The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index | 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 The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index Feridon Ghadimi, Sara Moghaddam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5916759/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Groundwater is a vital resource for drinking water, agriculture, and industry worldwide. Effective groundwater quality management is crucial for safeguarding public health and ensuring ecological sustainability. Hydrogeochemical data modeling is widely utilized to predict groundwater quality using various approaches. The method proposed in this study leverages an intelligent model combined with groundwater chemical compositions. Sampling was conducted from 175 drinking and agricultural wells in the Arak Plain. By utilizing hydrogeochemical data and performing correlation and sensitivity analyses, the key groundwater chemical compositions were identified: Ca²⁺, Cl⁻, EC, HCO₃⁻, K⁺, Mg²⁺, Na⁺, pH, SO₄²⁻, TDS, and NO₃⁻.The study predicted the Water Quality Index (WQI) values using the groundwater chemical composition data and an artificial neural network (ANN) model. The chemical compositions of the groundwater served as the model’s input, while the WQI was treated as the model’s output. To enhance the ANN's accuracy, several optimization algorithms were used, including: Simulated Annealing Algorithm (SAA), Firefly Algorithm (FA), Invasive Weed Optimization (IWO), and Shuffled Frog Leaping Algorithm (SFLA).The comparison of results indicated that the ANN-SAA model outperformed the other models. The R² and MSE values for the ANN-SAA model in predicting the WQI were for training data: R² = 0.8275, MSE = 0.0303 and test data: R² = 0.7357, MSE = 0.0371.These results demonstrate that the ANN-SAA model provides a reliable and accurate method for predicting groundwater quality index values, offering a valuable tool for groundwater quality assessment and management. Hydrochemical of groundwater Prediction of water quality index Artificial neural network Evolutionary algorithms Arak plain Iran. Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 22 Oct, 2025 Reviewers agreed at journal 07 Feb, 2025 Reviewers invited by journal 07 Feb, 2025 Editor assigned by journal 03 Feb, 2025 First submitted to journal 01 Feb, 2025 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-5916759","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":412560174,"identity":"b24e8295-0902-4808-b595-e4f70ae01ec3","order_by":0,"name":"Feridon Ghadimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACCSBKYJCo72dvAPIMLIjXwjiz5wBIiwSRWoAU44YbCVAuISAf3fzwxsMcC2bJmc+vbvhRIMHA396dgFeL4Z1jxhaJ2yTY+KVzym72AB0mcebsBvxaZiSYSQC18EjOzkm7wQPUYiCRS0hL+jeQFgmDm2fSbv4hRou8RA7YFgODG+zHbhNli4FETjHILwmSPTlst2UMJHgI+kV+RvrGmz+31SXwsx9/dvPNHxs5/vZeArYcgDN5DMAkXuVgWxrgTPYHBFWPglEwCkbByAQAGbZHsLYCVsQAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6058-9000","institution":"Arak University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Feridon","middleName":"","lastName":"Ghadimi","suffix":""},{"id":412560175,"identity":"7a491754-90bf-4d72-af80-e3ae9c54d776","order_by":1,"name":"Sara Moghaddam","email":"","orcid":"","institution":"Arak University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Moghaddam","suffix":""}],"badges":[],"createdAt":"2025-01-28 07:22:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5916759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5916759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75938779,"identity":"0739d71c-edc8-44b8-875e-ced01ee6726f","added_by":"auto","created_at":"2025-02-10 17:49:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1115667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5916759/v1_covered_16023269-bc05-4ccf-be49-c78148fafe71.pdf"}],"financialInterests":"","formattedTitle":"The application of Simulated Annealing Algorithm, Firefly Algorithm, Invasive Weed Optimization, and Shuffled Frog Leaping Algorithm for prediction of Water Quality Index","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hydrochemical of groundwater, Prediction of water quality index, Artificial neural network, Evolutionary algorithms, Arak plain, Iran. 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