## 4 Conclusions  
For the present case study, the application of the Multiple-Criteria Decision Analysis (MCDA) methodology through the PROMETHEE algorithm has proved an innovative \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{5}{c}{**Scenario 1: WEIGHT = 0.22; OTHERS = 011**} & \multicolumn{5}{c}{**Scenario 2: WEIGHT = 0.32; OTHERS = 0.01**} \\ \hline
**Rank** & **Alternativa** & \(\mathbf{\Theta}\) & \(\mathbf{\Theta^{+}}\) & \(\mathbf{\Theta}^{-}\) & **Alternativa** & \(\mathbf{\Theta}\) & \(\mathbf{\Theta^{+}}\) & \(\mathbf{\Theta}^{-}\) \\ \hline
1 & Ferrara & 0.7153 & 0.8064 & 0.0911 & Cento & 0.9452 & 0.9683 & 0.0231 \\ \hline
2 & Cento & 0.7093 & 0.8061 & 0.0968 & Ferrara & 0.9168 & 0.9538 & 0.037 \\ \hline
3 & Tresigallo & 0.5092 & 0.6746 & 0.1654 & Tresigallo & 0.696 & 0.7975 & 0.1015 \\ \hline
4 & Vigarano Mainarda & 0.2908 & 0.5771 & 0.2863 & Lagosanto & 0.4603 & 0.6797 & 0.2193 \\ \hline
5 & Argenta + Portomaggiore & 0.2279 & 0.534 & 0.306 & Vigarano Mainarda & 0.3006 & 0.5575 & 0.2569 \\ \hline
6 & Mirabello + Sant\({}^{\prime}\)Agostino & 0.219 & 0.5413 & 0.3222 & Argenta + Portomaggiore & 0.2083 & 0.5536 & 0.3454 \\ \hline
7 & Bondeno & 0.1597 & 0.4971 & 0.3375 & Mirabello + Sant\({}^{\prime}\)Agostino & 0.1207 & 0.4675 & 0.3468 \\ \hline
8 & Lagosanto & 0.1359 & 0.488 & 0.352 & Bondeno & 0.1154 & 0.507 & 0.3915 \\ \hline
9 & Copparo & 0.0416 & 0.4381 & 0.3965 & Comacchio & 0.1044 & 0.5017 & 0.3973 \\ \hline
10 & Comacchio & 0.0356 & 0.4378 & 0.4022 & Copparo & 0.076 & 0.4873 & 0.4113 \\ \hline
11 & Poggio Renatico & \(-\)0.0499 & 0.4041 & 0.454 & Mesola & \(-\)0.0919 & 0.3574 & 0.4493 \\ \hline
12 & Formignana & \(-\)0.0661 & 0.3555 & 0.4216 & Masi Torello & \(-\)0.1448 & 0.3309 & 0.4757 \\ \hline
13 & Voghiera & \(-\)0.1127 & 0.3295 & 0.4422 & Goro & \(-\)0.1563 & 0.3252 & 0.4815 \\ \hline
14 & Masi Torello & \(-\)0.1644 & 0.3064 & 0.4708 & Codigoro & \(-\)0.1861 & 0.3564 & 0.5426 \\ \hline
15 & Berra & \(-\)0.2045 & 0.2863 & 0.4908 & Poggio Renatico & \(-\)0.1924 & 0.3107 & 0.5031 \\ \hline
16 & Mesola & \(-\)0.2197 & 0.2787 & 0.4984 & Formignana & \(-\)0.1938 & 0.3064 & 0.5002 \\ \hline
17 & Ro & \(-\)0.226 & 0.2756 & 0.5016 & Voghiera & \(-\)0.2848 & 0.2607 & 0.5455 \\ \hline
18 & Goro & \(-\)0.2939 & 0.2416 & 0.5355 & Berra & \(-\)0.2929 & 0.2569 & 0.5498 \\ \hline
19 & Codigoro & \(-\)0.3041 & 0.268 & 0.5721 & Ro & \(-\)0.2949 & 0.2559 & 0.5507 \\ \hline
20 & Fiscaglia & \(-\)0.3385 & 0.2193 & 0.5578 & Fiscaglia & \(-\)0.594 & 0.1063 & 0.7003 \\ \hline
21 & Ostellato & \(-\)0.4816 & 0.1451 & 0.6267 & Ostellato & \(-\)0.7225 & 0.0418 & 0.7643 \\ \hline
22 & Jolanda di Savoia & \(-\)0.5829 & 0.0971 & 0.68 & Jolanda di Savoia & \(-\)0.7893 & 0.0087 & 0.798 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Sensitivity analysis on the exposure factor, ranking of alternatives (EXP: EXPOSURE). and promising operational tool. Its potential derives from the ability to both analyze information from various sources and jointly systematize data expressed in different units and scales. The application of this methodology has made it possible to rank the various municipalities in terms of the relative proneness to joint flood and seismic hazards. We recall that the objective of the methodology is not to quantify the safety level in absolute terms of the various municipalities. Its scope is, indeed, to provide useful information for decision makers and public authorities to define future intervention priorities. We further emphasize that, in the authors'opinion, the present study is original as it applies the PROMETHE algorithm for the first time to a multi-risk assessment of seismic and flood hazards. Depending on the territory to be studied, the relevant risks could be different, and therefore, different criteria must be used to express them. Nevertheless, the generalization to other multi-risk analyses and different case studies deserves further considerable efforts and thoughtful insights.