Development of a Fractional Orthopair Fuzzy MCDM Framework for Sustainable Water Resource Management in Lahore

Development of a Fractional Orthopair Fuzzy MCDM Framework for Sustainable Water Resource Management in Lahore | 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 Development of a Fractional Orthopair Fuzzy MCDM Framework for Sustainable Water Resource Management in Lahore Zhifang Han, Yujun Wang, Shah Zeb Khan, Muhammed I. Syam, Muhammad Rahim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6977696/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 28 You are reading this latest preprint version Abstract The evaluation of alternative water sources under uncertain, imprecise, and hesitant environments is a critical challenge in sustainable urban water management. To address this, the present study introduces a novel Fractional Orthopair Fuzzy (FOF) Sets-based multi-criteria decision-making (MCDM) framework that systematically integrates expert uncertainty using hesitancy degrees and fractional parameterization. Leveraging the technique for order preference by similarity to ideal solution (TOPSIS) with standardized parameters ( \(\:p=3\) , \(\:q=2\) ), the model incorporates Entropy-derived weights ( \(\:\omega\:\:=\:\text{0.355,0.287,0.358}\) ) and a newly formulated FOF weighted average (FOFWA) operator to robustly assess competing water sources. This approach is applied to the case of Lahore, Pakistan, a major metropolitan center confronting a worsening drinking water crisis driven by groundwater depletion (0.49–0.92 m/year), unchecked urban expansion, and pollution of the Ravi River despite receiving substantial annual rainfall (600–700 mm). The model evaluates three alternative water sources Surface Water, Groundwater, and Rainwater across the criteria of Quality, Availability, and Affordability. Results identify Rainwater as the most promising alternative, with a closeness coefficient of 0.8396, indicating its potential to serve as a cost-effective and sustainable resource. The integration of orthopair fuzzy logic and hesitancy metrics allows for nuanced modeling of vagueness in expert evaluations. Sensitivity analysis reveals a ranking deviation of less than 5% under varied weights, and comparative analysis indicates 92.5% decision accuracy and 97.5% stability, affirming the model’s reliability. This framework not only supports strategic interventions by the Water and Sanitation Agency (WASA) of Lahore but also offers a replicable tool for water resource planning in other vulnerable regions worldwide. Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing fractional orthopair fuzzy sets Multi criteria decision making Drinking water resource management TOPSIS method Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Jul, 2025 Reviews received at journal 24 Jul, 2025 Reviews received at journal 22 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 16 Jul, 2025 Reviewers agreed at journal 13 Jul, 2025 Reviewers agreed at journal 12 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviews received at journal 11 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviews received at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviews received at journal 07 Jul, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviews received at journal 27 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers agreed at journal 27 Jun, 2025 Reviewers invited by journal 27 Jun, 2025 Editor invited by journal 26 Jun, 2025 Editor assigned by journal 26 Jun, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 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. 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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-6977696","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":477947496,"identity":"3153bc0c-0104-40e1-9e62-4489ef66a3fc","order_by":0,"name":"Zhifang Han","email":"","orcid":"","institution":"Dalian Neusoft University of Information","correspondingAuthor":false,"prefix":"","firstName":"Zhifang","middleName":"","lastName":"Han","suffix":""},{"id":477947497,"identity":"78e78d65-276e-488e-8447-9e8978a9375a","order_by":1,"name":"Yujun Wang","email":"","orcid":"","institution":"Dalian Neusoft University of Information","correspondingAuthor":false,"prefix":"","firstName":"Yujun","middleName":"","lastName":"Wang","suffix":""},{"id":477947498,"identity":"8d37778c-465c-4e5f-b111-c4a9b2c25170","order_by":2,"name":"Shah Zeb Khan","email":"","orcid":"","institution":"University of Swat, KPK","correspondingAuthor":false,"prefix":"","firstName":"Shah","middleName":"Zeb","lastName":"Khan","suffix":""},{"id":477947499,"identity":"76ea5756-1705-44d4-bb51-fa53aeba2a5a","order_by":3,"name":"Muhammed I. 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To address this, the present study introduces a novel Fractional Orthopair Fuzzy (FOF) Sets-based multi-criteria decision-making (MCDM) framework that systematically integrates expert uncertainty using hesitancy degrees and fractional parameterization. Leveraging the technique for order preference by similarity to ideal solution (TOPSIS) with standardized parameters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p=3\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:q=2\\)\u003c/span\u003e\u003c/span\u003e), the model incorporates Entropy-derived weights (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\omega\\:\\:=\\:\\text{0.355,0.287,0.358}\\)\u003c/span\u003e\u003c/span\u003e) and a newly formulated FOF weighted average (FOFWA) operator to robustly assess competing water sources. This approach is applied to the case of Lahore, Pakistan, a major metropolitan center confronting a worsening drinking water crisis driven by groundwater depletion (0.49\u0026ndash;0.92 m/year), unchecked urban expansion, and pollution of the Ravi River despite receiving substantial annual rainfall (600\u0026ndash;700 mm). The model evaluates three alternative water sources Surface Water, Groundwater, and Rainwater across the criteria of Quality, Availability, and Affordability. Results identify Rainwater as the most promising alternative, with a closeness coefficient of 0.8396, indicating its potential to serve as a cost-effective and sustainable resource. The integration of orthopair fuzzy logic and hesitancy metrics allows for nuanced modeling of vagueness in expert evaluations. Sensitivity analysis reveals a ranking deviation of less than 5% under varied weights, and comparative analysis indicates 92.5% decision accuracy and 97.5% stability, affirming the model\u0026rsquo;s reliability. This framework not only supports strategic interventions by the Water and Sanitation Agency (WASA) of Lahore but also offers a replicable tool for water resource planning in other vulnerable regions worldwide.\u003c/p\u003e","manuscriptTitle":"Development of a Fractional Orthopair Fuzzy MCDM Framework for Sustainable Water Resource Management in Lahore","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-01 09:43:30","doi":"10.21203/rs.3.rs-6977696/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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