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Artificial Intelligence Integrated Quantum Picture Fuzzy Rough Decision Framework for Biodiversity-Oriented Ecosystem Investment Evaluation
Marco de decisión aproximada difusa con imagen cuántica integrada de inteligencia artificial para la evaluación de inversiones en ecosistemas orientada a la biodiversidad
DOI:
https://doi.org/10.15446/ing.investig.118711Keywords:
artificial intelligence, quantum recommender system, quantum theory, fuzzy rough sets, ecosystem investments (en)inteligencia artificial, sistema de recomendación cuántico, teoría cuántica, conjuntos aproximados difusos, inversiones en ecosistemas (es)
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This study identifies key indicators for strategic biodiversity decisions to improve the effectiveness of environmental ecosystem investments. A novel four-stage model is proposed. First, expert weights are determined using an artificial intelligence-based decision-making method. Second, missing evaluations are estimated for the strategic biodiversity decisions in ecosystem investments using an expert recommender system. Third, criteria weights for strategic biodiversity decisions are computed using quantum picture fuzzy rough set-based modified SWARA (M-SWARA). Finally, investment alternatives are ranked with QPFR-VIKOR. The main contribution of this study is the integration of artificial intelligence into fuzzy decision-making analysis to compute expert weights objectively, thereby enhancing the effectiveness and reliability of the proposed model. Results reveal that technological innovation and financial evaluation are the most significant indicators for strategic biodiversity decisions. Restoration projects and eco-friendly infrastructure are identified as the most suitable investment alternatives. The proposed framework provides managers and policymakers with a transparent, data-driven decision support tool for prioritizing technological capability development and financial feasibility analysis in biodiversity-oriented investment planning, contributing to more sustainable and strategically aligned ecosystem management practices.
Este estudio identifica indicadores clave para la toma de decisiones estratégicas en materia de biodiversidad, con el fin de mejorar la efectividad de las inversiones en ecosistemas ambientales. Se propone un modelo novedoso que consta de cuatro etapas. En primer lugar, los expertos se priorizan con un método de toma de decisiones basado en inteligencia artificial. En segundo lugar, las evaluaciones faltantes se estiman para las decisiones estratégicas de biodiversidad en las inversiones en ecosistemas mediante un sistema de recomendación de expertos. En tercer lugar, los pesos de los criterios para las decisiones estratégicas de biodiversidad se calculan utilizando SWARA modificado basado en conjuntos rugosos difusos de imágenes cuánticas (M-SWARA). Finalmente, las alternativas de inversión en ecosistemas se clasifican mediante QPFR-VIKOR. La principal contribución de este estudio es la integración de la metodología de inteligencia artificial en el análisis difuso de toma de decisiones para calcular objetivamente los pesos de los expertos, mejorando así la efectividad y la confiabilidad del modelo propuesto. Los hallazgos revelan que la innovación tecnológica y la evaluación financiera son los indicadores más significativos para las decisiones estratégicas de biodiversidad. Los proyectos de restauración y la infraestructura ecológicamente sostenible se identifican como las alternativas de inversión más adecuadas. Desde una perspectiva gerencial, los resultados proporcionan un marco estructurado de apoyo a la toma de decisiones que permite a los administradores y responsables de políticas priorizar el desarrollo de capacidades tecnológicas y el análisis de viabilidad financiera al planificar inversiones orientadas a la biodiversidad. El modelo propuesto también apoya estrategias de inversión más transparentes y basadas en datos, contribuyendo así a prácticas de gestión de ecosistemas más sostenibles y estratégicamente alineadas.
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Copyright (c) 2026 Peide Liu, Hasan Dinçer, Serhat Yüksel, Serkan Eti
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