Evaluation of spatial effects on confounding hydrogeochemical interactions in nitrate pollution using the SPAMAXAC method based on Machine Learning and Explainable Artificial Intelligence

Evaluation of spatial effects on confounding hydrogeochemical interactions in nitrate pollution using the SPAMAXAC method based on Machine Learning and Explainable Artificial Intelligence | 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 Evaluation of spatial effects on confounding hydrogeochemical interactions in nitrate pollution using the SPAMAXAC method based on Machine Learning and Explainable Artificial Intelligence Jagadish Kumar Mogaraju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8116269/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Nitrate contamination of groundwater is unsafe to humans and ecosystem sustainability, demanding effective initiatives focused on predictive frameworks for its monitoring and mitigation. In this study, we attempted to integrate machine learning and explainable artificial intelligence to predict nitrate levels and to highlight the spatial effects on hydrogeochemical interactions. We assessed machine learning models with and without spatial components for the years 2019 (19 models) and 2023 (19 models), respectively. LightGradient Boosting Machine and Linear Regression model provided relatively better accuracy with an R 2 value ranging from 0.44 to 0.51, reflecting the intricacies that these models captured among the hydrogeochemical features. We find that the SHAP provided inflated R 2 values (up to 28%) compared to machine learning derived values, raising concerns with XAI utility in groundwater studies. We observed that the chloride and bicarbonates remained dominant predictors of nitrate when the spatial effect was considered. Without spatial effects, we find that phosphate and pH are dominant predictors of nitrate (2019) and that chloride and sodium are dominant predictors (2023). We proposed a SPAMAXAC method that interweaves the Spatial effect, ML, SHAP, and correlation to address the problem to a relatively reasonable extent. We conclude that the use of ML integrated with XAI for groundwater studies holds immense potential, but caution should be ensured concerning overstated interpretations. Pollution Groundwater quality Prediction Regression Spatial effect GIS XAI ML Correlation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviews received at journal 29 Nov, 2025 Reviewers agreed at journal 29 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 26 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 19 Nov, 2025 First submitted to journal 19 Nov, 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. 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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-8116269","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":552749431,"identity":"b185f2a4-054c-47c6-9dfa-3010d5f261d9","order_by":0,"name":"Jagadish Kumar 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