Choquet integral-based GRA method with linguistic interval-valued q-Rung orthopair fuzzy sets

Linguistic interval-valued q-rung orthopair fuzzy (LIVq-ROF) sets offer a powerful framework for modeling uncertainty and vagueness in complex decision-making environments. This study leverages the expressive strength of LIVq-ROF sets to develop novel aggregation operators-specifically, the LIVq-ROF Choquet integral averaging and geometric operators-which are designed to capture interdependencies among attributes in multiple attribute group decision-making (MAGDM) scenarios. The theoretical properties of these operators are rigorously established. Building on this foundation, we propose a Choquet integral-based grey relational analysis (GRA) method tailored for MAGDM under uncertainty. The proposed model is applied to a real-world case study involving the selection of the optimal neural network model for predicting crop yields. Results demonstrate the model’s effectiveness in identifying the best-performing alternative. A thorough sensitivity analysis and comparison with existing approaches confirm the robustness and superior performance of the proposed method.