A Spherical Fuzzy WASPAS Approach with Dombi Aggregation for Satellite Imagery Tool Selection in Climate Monitoring Applications

A Spherical Fuzzy WASPAS Approach with Dombi Aggregation for Satellite Imagery Tool Selection in Climate Monitoring Applications | 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 A Spherical Fuzzy WASPAS Approach with Dombi Aggregation for Satellite Imagery Tool Selection in Climate Monitoring Applications Vinod Kumar R, Hemavathi P, Muralikrishna P, Hasan Dincer, Serhat Yuksel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8486126/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In this paper, a new decision-making model is suggested to be applied when choosing satellite imagery analysis tools in climatic monitoring uses. The method combines the Weighted Aggregated Sum Product Assessment (WASPAS) model with Dombi aggregation operators in the context of the spherical fuzzy set that is successfully used to address the uncertainty, hesitation and imprecision common in expert ratings. The criteria used to select the key decisions are the accuracy of the resolution, efficiency in time, the ability to integrate data, and the applicability in climate analysis. Spherical fuzzy sets improve the ability of the model to deal with vague information and the use of Dombi operators enables the aggregation behavior to be adjusted. The effectiveness and strength of the proposed method is confirmed and proven in a case study and comparative analysis. The findings point to its applicability as a valid and total strategy of technology choice in environmental as well as remote sensing arenas. Earth and environmental sciences/Climate sciences Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Spherical Fuzzy Set WASPAS Method Dombi Aggregation Operator Multi-Criteria Decision Making Satellite Imagery Analysis Climate Monitoring Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 05 Mar, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviews received at journal 29 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 20 Jan, 2026 Editor invited by journal 02 Jan, 2026 Editor assigned by journal 31 Dec, 2025 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 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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