Hydrochemical Characterization and Water Quality Modelling of the Axios River (Northern Greece) Using Factor Analysis, Artificial Neural Networks and Multiple Linear Regression

Hydrochemical Characterization and Water Quality Modelling of the Axios River (Northern Greece) Using Factor Analysis, Artificial Neural Networks and Multiple Linear Regression | 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 Hydrochemical Characterization and Water Quality Modelling of the Axios River (Northern Greece) Using Factor Analysis, Artificial Neural Networks and Multiple Linear Regression Vasiliki Kinigopoulou, Christos Mattas, Ioannis Vrouhakis, Evangelos Hatzigiannakis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9232782/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 investigates the hydrochemical characteristics and controlling factors of water quality along the Axios River (Northern Greece) using a combination of statistical methods and artificial neural networks (ANN). It also aims to predict Electrical conductivity (EC) which is a dependent physico-chemical parameter. Electrical Conductivity is a key indicator of river water quality, reflecting both natural hydrogeochemical processes and anthropogenic pressures. Water samples were collected from three monitoring stations (Idomeni, Prochoma, and Malgara) during the period 2018–2023 and were analyzed to assess spatial and temporal variability and to identify dominant hydrochemical processes. Hydrochemical analysis revealed that river water is predominantly characterized by Ca–Mg–HCO₃ facies, indicating strong geogenic control associated with carbonate weathering, while spatial variations along the river course were generally limited. Pearson correlation analysis and factor analysis were applied to explore linear relationships among major ions and EC, and Multiple Linear Regression (MLR) models were subsequently developed for EC prediction. Although MLR demonstrated satisfactory performance, its predictive capability varied among monitoring stations, reflecting differences in hydrochemical complexity. To investigate potential nonlinear relationships, ANN models based on a Multilayer Perceptron architecture were implemented and evaluated using independent training and testing datasets. The relative importance of input variables was assessed through ANN-based Independent Variable Importance analysis, which was further employed as a feature selection tool. The refined ANN models showed improved predictive performance and enhanced interpretability, particularly at stations influenced by mixed hydrochemical processes. The comparative evaluation of linear and nonlinear modelling approaches indicates that EC in the Axios River is largely governed by linear geochemical relationships, while ANN-based methods can capture subtle nonlinear effects and provide additional insight when combined with classical statistical analysis. The results highlight the complementary role of machine learning techniques in river water quality assessment and support their use as exploratory and interpretative tools rather than as standalone predictive solutions. Hydrochemistry Water quality modelling Electrical conductivity Artificial neural networks Multivariate statistical analysis Feature selection Machine learning River systems 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. 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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-9232782","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625540290,"identity":"f08e1558-cbe3-4289-b46d-2507473a52c2","order_by":0,"name":"Vasiliki Kinigopoulou","email":"","orcid":"","institution":"International Hellenic University","correspondingAuthor":false,"prefix":"","firstName":"Vasiliki","middleName":"","lastName":"Kinigopoulou","suffix":""},{"id":625540291,"identity":"9d0fa919-1736-40f1-b3a4-c66bf05743f0","order_by":1,"name":"Christos Mattas","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBADORgjgRjVjA0HGBiMoRwD4rUkNhCthX/a4eePP9TUpc93bz7A8LPtTx4D/9oHeLVI3E4zbDhw7HDuxjPHEhh72wyKGSSeG+DVYiCdANTCdiB344wcAwbeNoPEBolj+B1mIJ3+seHAv7p0w/lvDBj/Eqclx7DhYBtzgrwEjwEz2Bb+NvxaJG7nFM4423fYcANPWsJhmXPGiW0SbPi18M9O3/Ch4ludvHz74YMP35TJJfbzE3AYwoUHGBgOgBhsEglEapFvgFt8gEgto2AUjIJRMFIAAFbsSxGOwor9AAAAAElFTkSuQmCC","orcid":"","institution":"Aristotle University of Thessaloniki","correspondingAuthor":true,"prefix":"","firstName":"Christos","middleName":"","lastName":"Mattas","suffix":""},{"id":625540292,"identity":"eb109a18-68e1-41ff-a6fd-892cb8ad907a","order_by":2,"name":"Ioannis Vrouhakis","email":"","orcid":"","institution":"Hellenic Agricultural Organization DIMITRA","correspondingAuthor":false,"prefix":"","firstName":"Ioannis","middleName":"","lastName":"Vrouhakis","suffix":""},{"id":625540293,"identity":"5030bf21-0140-4971-91cd-8cb9958e545e","order_by":3,"name":"Evangelos Hatzigiannakis","email":"","orcid":"","institution":"Hellenic Agricultural Organization DIMITRA","correspondingAuthor":false,"prefix":"","firstName":"Evangelos","middleName":"","lastName":"Hatzigiannakis","suffix":""}],"badges":[],"createdAt":"2026-03-26 10:24:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9232782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9232782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109324824,"identity":"f4e9c8da-5184-492c-b861-91494656d58f","added_by":"auto","created_at":"2026-05-15 14:25:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":766268,"visible":true,"origin":"","legend":"","description":"","filename":"KinigopoulouetalHydrochemicalCharacterizationandWaterQualityModellingoftheAxiosRiverrev1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9232782/v1_covered_b475f54b-3924-4781-ba3d-092edc3ca05d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hydrochemical Characterization and Water Quality Modelling of the Axios River (Northern Greece) Using Factor Analysis, Artificial Neural Networks and Multiple Linear Regression","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":"Hydrochemistry, Water quality modelling, Electrical conductivity, Artificial neural networks, Multivariate statistical analysis, Feature selection, Machine learning, River systems","lastPublishedDoi":"10.21203/rs.3.rs-9232782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9232782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the hydrochemical characteristics and controlling factors of water quality along the Axios River (Northern Greece) using a combination of statistical methods and artificial neural networks (ANN). It also aims to predict Electrical conductivity (EC) which is a dependent physico-chemical parameter. Electrical Conductivity is a key indicator of river water quality, reflecting both natural hydrogeochemical processes and anthropogenic pressures. Water samples were collected from three monitoring stations (Idomeni, Prochoma, and Malgara) during the period 2018\u0026ndash;2023 and were analyzed to assess spatial and temporal variability and to identify dominant hydrochemical processes.\u003c/p\u003e \u003cp\u003eHydrochemical analysis revealed that river water is predominantly characterized by Ca\u0026ndash;Mg\u0026ndash;HCO₃ facies, indicating strong geogenic control associated with carbonate weathering, while spatial variations along the river course were generally limited. Pearson correlation analysis and factor analysis were applied to explore linear relationships among major ions and EC, and Multiple Linear Regression (MLR) models were subsequently developed for EC prediction. Although MLR demonstrated satisfactory performance, its predictive capability varied among monitoring stations, reflecting differences in hydrochemical complexity.\u003c/p\u003e \u003cp\u003eTo investigate potential nonlinear relationships, ANN models based on a Multilayer Perceptron architecture were implemented and evaluated using independent training and testing datasets. The relative importance of input variables was assessed through ANN-based Independent Variable Importance analysis, which was further employed as a feature selection tool. The refined ANN models showed improved predictive performance and enhanced interpretability, particularly at stations influenced by mixed hydrochemical processes.\u003c/p\u003e \u003cp\u003eThe comparative evaluation of linear and nonlinear modelling approaches indicates that EC in the Axios River is largely governed by linear geochemical relationships, while ANN-based methods can capture subtle nonlinear effects and provide additional insight when combined with classical statistical analysis. The results highlight the complementary role of machine learning techniques in river water quality assessment and support their use as exploratory and interpretative tools rather than as standalone predictive solutions.\u003c/p\u003e","manuscriptTitle":"Hydrochemical Characterization and Water Quality Modelling of the Axios River (Northern Greece) Using Factor Analysis, Artificial Neural Networks and Multiple Linear Regression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 17:44:43","doi":"10.21203/rs.3.rs-9232782/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"899caebc-df4b-4b6c-99cd-1f6c35ace280","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-15T14:19:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T20:02:58+00:00","index":22,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T22:12:16+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T14:25:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 17:44:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9232782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9232782","identity":"rs-9232782","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}