Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables

Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables | 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 Machine Learning for Groundwater Storage Prediction: Leveraging Climatic Variables Saleh Md. Abu, Rasel H. M. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4452205/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Once abundant and readily available, groundwater (GW) is now dwindling at an alarming rate. This vital resource is under growing pressure from both natural and human-induced factors. Groundwater Level (GWL) is closely related to Groundwater Storage (GWS) thus the decline in GWL creates a shortage in GWS. This research developed a robust predictive model for GWS in Rajshahi district, Bangladesh, for the period 2001–2022 using six climatic variables, namely, Mean Temperature, Cloud Coverage, Humidity (percent), Solar Radiation, Sunshine, and Wind Speed. Three Machine Learning (ML)-based regression models- Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosting Machine (GBM) were applied for this purpose. Results showed that the accuracy level was quite high while RF regression was plugged into the observed dataset (R 2 = 0.80). Moreover, among the six climatic variables, cloud coverage, humidity, and wind speed contributed 87.4% altogether to predict the GWS. These findings offer valuable insights not only for understanding the GWS dynamics in Rajshahi district but also for informing sustainable management strategies. By providing decision-makers with a clear understanding of the key climatic drivers and their impact, this research empowers them to implement effective interventions and conservation measures to ensure the long-term availability of this critical resource. Groundwater Storage Random Forest Support Vector Machine Gradient Boosting Machine Bangladesh Full Text Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.docx DisclosureofConflictingInterests.docx ResearchHighlights.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 23 May, 2024 Submission checks completed at journal 22 May, 2024 First submitted to journal 21 May, 2024 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-4452205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305809881,"identity":"ebb23710-f896-4fc5-befc-2f0e182dab67","order_by":0,"name":"Saleh Md. 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