Evidence map›Paper›PMID 42000864›Full record

ArticleScientific reports2026

A spherical fuzzy WASPAS approach with Dombi aggregation for satellite imagery tool selection in climate monitoring applications.

R Vinod Kumar, P Hemavathi, P Muralikrishna, Hasan Dincer, Serhat Yuksel

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

R Vinod KumarDepartment of Mathematics, Rajalakshmi Engineering College (Autonomous), Thandalam, Chennai, 602 105, India. vinodkumar.r@rajalakshmi.edu.in.
P HemavathiDepartment of Mathematics, Saveetha Institute of Medical and Technical Sciences, SIMATS, Thandalam, 602 105, India.
P MuralikrishnaPG and Research Department of Mathematics, Muthurangam Government Arts College (Autonomous), Vellore, 632002, India.
Hasan DincerIstanbul Medipol University, School of Business, Kavacık South Campus, Beykoz, 34810, Istanbul, Turkey.
Serhat YukselIstanbul Medipol University, School of Business, Kavacık South Campus, Beykoz, 34810, Istanbul, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Climate monitoringDombi aggregation operatorMulti-criteria decision makingSatellite imagery analysisSpherical fuzzy setWASPAS method

Identifiers

PMID42000864
PMCPMC13254142

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