## 1 Introduction  
Flood vulnerability is part of the flood risk concept [1]. Vulnerability can be divided into three components: (i) exposure to a natural event, (ii) susceptibility or resistance, and (iii) adaptive capacity, also known as resilience. In recent years, there has been rapid methodological development in flood vulnerability assessment studies [2]. The integration of multicriteria decision analysis (MCDA) into a geographic information system (GIS) allows the development of a GIS multicriteria decision support framework for flood vulnerability assessment [3; 4; 5]. Some of these studies use flood vulnerability indices. The design of a flood vulnerability index can be divided into several stages: conceptual framework, structural design, scale of analysis, indicator selection, normalisation,weighting, and aggregation [6]. Flood vulnerability is multidimensional and has spatial indicators with a complex variety of sources, types, and scales. The hierarchical model used criteria divided into sub-criteria that share the same core dimension of vulnerability [6]. The relative importance of vulnerability indices was calculated using weighting methods, such as principal component analysis (PCA) or analytic hierarchy process (AHP) [7; 2; 8; 9; 10; 11; 12; 13; 14]. The AHP is a multi-criteria decision-making tool and has three underlying concepts [15]: (i) structuring the complex decision problem as a hierarchy of objective, criteria and sub-criteria; (ii) pairwise comparison (i.e., judgement) of elements at each level of the hierarchy; and (iii) vertical synthesis of the judgements across the different levels of the hierarchy. The AHP method was used in most studies on flood risk management through MCDA with application to the risk, hazard and vulnerability assessment [16]. AHP has been used in several studies to assess either flood hazard [17; 18], flood risk [2; 19; 20; 21; 22; 23], and flood vulnerability [10; 24]. Although AHP was initially developed for an Individual Decision Making (IDM) context [2; 25], other authors [4; 12; 26] applied a methodological extension for coping with multi-person Group Decision Making (GDM) context. Compared to individual decision-making, group research has the potential to gather more complete information. Since the group has several members with different points of view and different experiences on a particular issue, it will improve the decision-making. A GDM with experts in the field of hydrology was used for potential flood-prone areas mapping [27], which is following the implementation of the EU Flood Risk Directive [28]. The assessment of vulnerability addressed in the Flood Risk Management Plans (FRMP) at the river basin district level issued by the Portuguese Environment Agency, adopts the GIS-MCDA methodology developed by [PERSON] et al. [29]. However, to the best of our knowledge, except for the study by [PERSON] et al. [12], no other study has considered group decision-making for the assessment of flood vulnerability. In that study, the experts were asked to construct the problem structure and establish the structure of the vulnerability index, and the questionnaire was answered in workshops. In this approach, the experts were actively involved in all steps of the vulnerability modelling process and thus had a great influence on the final index, but it was very time-consuming for all the participants. When dealing with AHP-GDM, there is a need to integrate the results. Two of the most traditional aggregation approaches are Aggregation of Individual Judgements (AIJ) and Aggregation of Individual Priorities (AIP): (i) In AIJ, the individual pairwise comparison matrices are first aggregated to obtain a group matrix. The priority vector is then derived from this new matrix. (ii) In AIP, the priority vectors are first obtained for each individual, then aggregated to obtain the group priorities. When using GDM, it is necessary to ensure the consistency measure of each expert itself and the consistency measure of the whole group [30]. [PERSON] et al.