Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria

Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria | 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 Article Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria Loubna Nefla, Amira Bergal, Warda Boumaraf, Samira Gheid, Chahrazed Boukssiba, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7051963/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Given its availability amid the increasing scarcity of surface freshwater, groundwater has become a vital and increasingly relied-upon resource, especially in semi-arid and arid regions. Thus, to ensure groundwater complies with standards before use, continuous monitoring and comprehensive quality assessment are essential. This study aimed to assess the quality of groundwater (GW) in the Skikda aquifer, northeastern Algeria, for irrigation using irrigation water quality indices (IWQIs), multivariate statistical analysis,and machine learning algorithms (MLAs): Random Forest regression (RF), Extreme Gradient Boosting regression (XGBR), and Adaptive Boosting Regression (ABR), integrated with SHAP analysis. Forty-four groundwater samples were collected from the study area during summer and winter seasons andanalysed for temperature, pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), and concentrations of calcium (Ca²⁺), magnesium (Mg²⁺),sodium (Na⁺),potassium (K⁺), chloride (Cl⁻),bicarbonate (HCO₃⁻), sulfate (SO₄²⁻), and nitrate (NO₃⁻).The dominating hydrochemical facies in the study area were Mg-Ca-SO 4 , accompanied by the Sodium-Chloride (Na-Cl).Principle Component Analysis (PCA) for summer and winter datasets identified four key components suggesting a strong correlation between variables and factors, with PCA indicating that geochemical processes, such as rock0water interaction and dissolution of evaporite minerals, control the groundwater’s chemical composition.Groundwater quality for irrigation varied across the samples, with most exhibiting moderate to high constraints based on IWQI. Sodium Adsorption Ratio (SAR) and Permeability Index (PI) suggested excellent to good water quality,while Sodium Percent (Na%) and Soluble Sodium Percentage (SSP) indicate a small but significant fraction of inappropriate samples.Magnesium Hazard (MH) and SSP indicated that most samples were safe.Compared to winter, summer samples showed slightly poorer quality (higher Na%, SSP, and lower IWQI), likely due toevaporative solute concentration. Random Forest (RF) modelshowed superior predictive accuracy for all Water Quality Indices (WQIs), with strong validation results for both seasons. These results highlight RF's effectiveness in predicting WQIs and highlight the influence of seasonal geochemical processes on groundwater quality, requiring the development of management strategies for sustainable irrigation. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology irrigation groundwater quality hydrogeochemical properties machine learning algorithms GIS techniques Algeria Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 25 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers invited by journal 23 Jul, 2025 Editor assigned by journal 23 Jul, 2025 Editor invited by journal 11 Jul, 2025 Submission checks completed at journal 09 Jul, 2025 First submitted to journal 09 Jul, 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. 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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-7051963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489813363,"identity":"197d81f1-0688-44da-bafd-03b45de142a1","order_by":0,"name":"Loubna Nefla","email":"","orcid":"","institution":"Laboratory of Biodiversity and Ecosystem Pollution, Department of Biology, Faculty of Sciences, Chadli Bendjedid University, El-Tarf, , BP 73, 36000, EL TARF, Algeria","correspondingAuthor":false,"prefix":"","firstName":"Loubna","middleName":"","lastName":"Nefla","suffix":""},{"id":489813364,"identity":"97b5cb4d-9bb5-40c4-a82e-69a10fdba307","order_by":1,"name":"Amira Bergal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie2QMUvDQBiG7yg0y2uzXkDiX7gQaBepfyUQiEt0FKFgL0tcqq7x31zI0MV2zqgITg5xUynqF9suwjWOgvfAfdx73MN7HGMWy58E33PfpaHbTZsa1u9W4Km1Asq8+JUi9SZ2Kq7aKx/P38cI65OyAjvEkbN4UOzswqgIPYiDxXWMYX0akZIAOA4UW+6o0Rh62axHSirL24+KHpY49zyH0TjQGL1lsynCgpSCfQLuE1c8F0ZFUgtXrxWkSKVuqBQiaRVpVIJqEHuZmkPcPbdKDNTUEi0jo+LPr8oXtZr47mUaNhEb+84NtTQ7foz1aPH856m5Y8uq84bFYrH8Z74Ab1lM5MT9OPEAAAAASUVORK5CYII=","orcid":"","institution":"Laboratory of Environmental Sciences and Agroecology. 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Thus, to ensure groundwater complies with standards before use, continuous monitoring and comprehensive quality assessment are essential. This study aimed to assess the quality of groundwater (GW) in the Skikda aquifer, northeastern Algeria, for irrigation using irrigation water quality indices (IWQIs), multivariate statistical analysis,and machine learning algorithms (MLAs): Random Forest regression (RF), Extreme Gradient Boosting regression (XGBR), and Adaptive Boosting Regression (ABR), integrated with SHAP analysis. Forty-four groundwater samples were collected from the study area during summer and winter seasons andanalysed for temperature, pH, electrical conductivity (EC), turbidity, total dissolved solids (TDS), and concentrations of calcium (Ca\u0026sup2;⁺), magnesium (Mg\u0026sup2;⁺),sodium (Na⁺),potassium (K⁺), chloride (Cl⁻),bicarbonate (HCO₃⁻), sulfate (SO₄\u0026sup2;⁻), and nitrate (NO₃⁻).The dominating hydrochemical facies in the study area were Mg-Ca-SO\u003csub\u003e4\u003c/sub\u003e, accompanied by the Sodium-Chloride (Na-Cl).Principle Component Analysis (PCA) for summer and winter datasets identified four key components suggesting a strong correlation between variables and factors, with PCA indicating that geochemical processes, such as rock0water interaction and dissolution of evaporite minerals, control the groundwater\u0026rsquo;s chemical composition.Groundwater quality for irrigation varied across the samples, with most exhibiting moderate to high constraints based on IWQI. Sodium Adsorption Ratio (SAR) and Permeability Index (PI) suggested excellent to good water quality,while Sodium Percent (Na%) and Soluble Sodium Percentage (SSP) indicate a small but significant fraction of inappropriate samples.Magnesium Hazard (MH) and SSP indicated that most samples were safe.Compared to winter, summer samples showed slightly poorer quality (higher Na%, SSP, and lower IWQI), likely due toevaporative solute concentration. Random Forest (RF) modelshowed superior predictive accuracy for all Water Quality Indices (WQIs), with strong validation results for both seasons. These results highlight RF's effectiveness in predicting WQIs and highlight the influence of seasonal geochemical processes on groundwater quality, requiring the development of management strategies for sustainable irrigation.\u003c/p\u003e","manuscriptTitle":"Innovative machine learning and GIS-integrated framework for predicting irrigation water quality through the insights from Semi-arid Coastal Aquifers in Northeastern Algeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 13:56:50","doi":"10.21203/rs.3.rs-7051963/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-29T08:55:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T20:56:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T17:49:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202579433619771197387510961222048602516","date":"2025-07-28T12:56:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32448984923965227925722699940944335239","date":"2025-07-25T11:18:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243116082508584448894986179690685599308","date":"2025-07-23T14:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224606522616369998534059761440411268862","date":"2025-07-23T11:40:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T11:11:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T11:01:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T23:40:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-09T20:39:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-07-09T20:34:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"13ee133a-f25f-44a0-86f2-e847e9351b4f","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52003244,"name":"Earth and environmental sciences/Environmental sciences"},{"id":52003245,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2026-02-02T16:16:24+00:00","versionOfRecord":{"articleIdentity":"rs-7051963","link":"https://doi.org/10.1038/s41598-025-25461-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-28 15:58:20","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-07-25 13:56:50","video":"","vorDoi":"10.1038/s41598-025-25461-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-25461-y","workflowStages":[]},"version":"v1","identity":"rs-7051963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7051963","identity":"rs-7051963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}