Emergency communication support is a critical prerequisite for effective disaster response. To address the problems of one-sided indicator weighting and the uncertain quantification of qualitative indicators in emergency communication network evaluation, this study proposes a combined weighting–cloud model–TOPSIS framework. First, an evaluation indicator system is constructed from four dimensions: transmission performance, coverage capability, service quality, and survivability. Second, an FAHP–entropy combined weighting strategy is used to integrate expert knowledge and data-driven information. Third, a backward cloud generator is introduced to quantify qualitative indicators while preserving both fuzziness and randomness; triangular fuzzy numbers and relative preference relations are further used to convert cloud outputs into comparable scalar values. Finally, TOPSIS is applied to rank alternative emergency communication plans. A flood rescue case study and additional sensitivity tests show that the ranking of the three plans remains stable under perturbations of weights, input data, and indicator inclusion. This paper also provides the raw input data, expert scoring details, simulation assumptions, and a comparison with other MCDA methods to improve reproducibility.
Wang et al. (Wed,) studied this question.
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