A normal cloud model-based decision making method under multi-granular probabilistic linguistic environment for evaluating of wetland ecosystem services

An accurate understanding of wetlands and the various functions they provide to humans through the evaluation of wetland ecosystem services values (WESVs) is essential for the rational and effective management of wetlands. In practice, obtaining quantitative data on wetlands is a challenge. Therefore, a new and systematic multi-attribute group decision-making method (MAGDM) was constructed. After collecting WESV probabilistic linguistic evaluation data from multiple experts, the method was used to compare and rank wetlands with known data and wetlands with unknown data, so as to indirectly obtain WESV evaluation. Specifically, the concept of multi-granular probabilistic linguistic cloud (MPLC) with its basic algorithm, deviation measure, and cloud information fusion tool is first presented. It is used to deal with the problem of multi-granular linguistic information due to the different knowledge backgrounds of experts. Secondly, two models for determining attribute weights and expert weights are constructed to provide solutions to the problem of unknown weight information. By improving the final ranking method of the MULTIMOORA method and taking into account the risk-averse psychological activities of the experts, the prospect theory-based MULTIMOORA method under cloud environment is proposed. Finally, some wetlands are used as examples to demonstrate the applicability of the constructed MAGDM method. The simulation results show that the proposed method is computationally more straightforward and robust than before, and the basic idea is logical and understandable. In addition, corresponding sensitivity and comparative analyses were further conducted to demonstrate the superiority and effectiveness of the proposed method.