OPTIMIZING NON-INVASIVE REMOTE SENSING FOR GEOTHERMAL EXPLORATION WITH T-SPHERICAL DUAL HESITANT FUZZY DECISION MODEL

Michael Sandra, Samayan Narayanamoorthy, Krishnan Suvitha, Dragan Pamucar, Daekook Kang

DOI Number
10.22190/FUME241116016S
First page
Last page

Abstract


Traditional geothermal detection methods, such as extensive ground-based surveys and drillings, are often costly, time-consuming, and environmentally intrusive. To address these challenges, this study presents a novel hybrid fuzzy multi-criteria decision-making model to evaluate and prioritize non-invasive, cost-effective remote sensing (RS) techniques. This model uses T-spherical dual-hesitant fuzzy set to manage the inherent ambiguities in the evaluation of multiple criteria. The logarithmic percentage change-driven objective weighting technique assigns the relative importance of criteria, and the multiple triangle scenarios-II methodology helps in comprehensive evaluation and ranking. By incorporating expert judgment and addressing inherent uncertainties, this model provides a systematic framework for optimizing RS technique selection. Findings indicate that thermal infrared imaging, with a significance score of 0.7187, holds transformative potential for geothermal energy development. Sensitivity and comparative analyses further confirm the robustness of this approach. This research offers a valuable resource for energy developers and policymakers aiming to leverage RS technologies for efficient geothermal resource management and development.


Keywords

Multi-criteria decision making, T-spherical dual-hesitant fuzzy set, Multiple triangles scenarios-II, Logarithmic percentage change-driven objective weighting, Geothermal detection

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References


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