A hesitancy fuzzy TOPSIS model for sustainable rainwater harvesting venue selection in rural agriculture applications

Selecting suitable reservoir locations for agricultural cultivation in green ecosystems is a complex decision-making problem due to uncertainty, imprecise information, and interdependent evaluation criteria. Conventional decision-making approaches often fail to effectively model such uncertainty and structural complexity. This study presents an integrated decision-making framework that combines the signless Laplacian energy of hesitancy fuzzy graphs with the Technique for Order Preference by Similarity to Ideal Solution to improve reservoir venue selection. Hesitancy fuzzy graphs are developed to represent uncertain relationships between potential reservoir sites and agricultural suitability factors. The signless Laplacian energy of these graphs is computed to quantify structural characteristics such as variability, connectivity, and complexity among alternatives. These energy values are incorporated into the decision-making process to enhance the evaluation and ranking of reservoir locations using the Technique for Order Preference by Similarity to Ideal Solution. The results indicate that signless Laplacian energy effectively captures the complexity of hesitancy fuzzy relationships and provides stable and meaningful rankings of alternatives. The proposed framework improves the reliability and robustness of decision-making in agricultural water management. This study concludes that integrating advanced fuzzy graph theory with multi-criteria decision analysis offers a practical and efficient approach for sustainable reservoir planning in green ecosystems.

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