Identification Of Groundwater Potential Zones in Outer Himalaya’s of Himachal Pradesh Una District Through Remote Sensing, AHP And MIF Techniques

Identification Of Groundwater Potential Zones in Outer Himalaya’s of Himachal Pradesh Una District Through Remote Sensing, AHP And MIF Techniques | 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 Identification Of Groundwater Potential Zones in Outer Himalaya’s of Himachal Pradesh Una District Through Remote Sensing, AHP And MIF Techniques Arvind kumar, Ray Singh Meena, Chander Kant This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8392419/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 23 You are reading this latest preprint version Abstract Groundwater is vital for domestic, agricultural, and industrial needs, especially in regions with limited surface water due to erratic rainfall. A study in Una district, Himachal Pradesh, utilized Geographic Information System (GIS) tools combined with Analytical Hierarchy Process (AHP) and Multi-Influencing Factor (MIF) techniques to identify groundwater potential zones. Thematic layers—including slope, geology, land use/land cover, drainage density, soil texture, rainfall, geomorphology, and elevation—were developed using ArcGIS, drawing data from sources like the Survey of India and satellite imagery. Each layer was weighted based on its influence on groundwater recharge. AHP employed pairwise comparisons to assess factor importance, while MIF considered both primary and secondary influences. Validation against 29 well observations using Receiver Operating Characteristic (ROC) curves showed the AHP model had an Area Under Curve (AUC) of 85%, indicating higher predictive accuracy compared to MIF's 73.7%. The findings offer valuable insights for sustainable groundwater management and can assist policymakers in prioritizing areas for recharge and conservation efforts. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Groundwater Potential: Thematic Layers Remote Sensing GIS Outer Himalaya’s Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 11 Jan, 2026 Reviewers agreed at journal 10 Jan, 2026 Reviewers agreed at journal 10 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Editor invited by journal 08 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 02 Jan, 2026 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-8392419","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":573201270,"identity":"8a3d7949-79bd-47a1-94a6-9cfe46d71e52","order_by":0,"name":"Arvind 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A study in Una district, Himachal Pradesh, utilized Geographic Information System (GIS) tools combined with Analytical Hierarchy Process (AHP) and Multi-Influencing Factor (MIF) techniques to identify groundwater potential zones. Thematic layers\u0026mdash;including slope, geology, land use/land cover, drainage density, soil texture, rainfall, geomorphology, and elevation\u0026mdash;were developed using ArcGIS, drawing data from sources like the Survey of India and satellite imagery. Each layer was weighted based on its influence on groundwater recharge. AHP employed pairwise comparisons to assess factor importance, while MIF considered both primary and secondary influences. Validation against 29 well observations using Receiver Operating Characteristic (ROC) curves showed the AHP model had an Area Under Curve (AUC) of 85%, indicating higher predictive accuracy compared to MIF's 73.7%. 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