## 4 Discussion  
On the basis of the applicative examples provided in Section 3, some comments are drawn on the proposed monitoring procedure. 1. Strategy 1 assumed that a rare behavior precedes a critical event, which is a strong assumption indeed. Figure 8 shows, however, that this strategy successfully found one warning point at the chosen monitoring time. It is interesting to note that, as in the reference case (Figure 8a), a large ground settlement also occurred after the precursor window in the warning point (Figure 8b). It can also be observed that the rarity of the reference behavior led the trained SOM network to find only one similar event, which is an index of the selectivity of the monitoring system. 2. Strategy 2 assumes that analogous events have similar timeseries precursors. A reference window was chosen in this case on the basis of the anomalous trend of the timeseries that followed. This strategy highlighted 11 warning points, all of them experiencing a comparatively large displacement developed in a short time after the precursor window. Again, it is to note that a limited number of warning points were detected by the monitoring system. 3. The similarity between timeseries depends on the monitoring hyperparameters (e.g., length of the time window, scattering threshold). On the other hand, the choice of the hyperparameters can be calibrated according to the phenomenon to be monitored, provided that the main characteristics of the latter are known. It is expected in fact that different initial development times characterize different geological phenomena. In light of this, the monitoring system could even be carried out in parallel with different hyperparameters (e.g., the length of the time window) to forecast different geological events at their beginning. 4. The early-warning procedure here proposed is flexible in the application under different monitoring strategies, as well as different hyperparameters. 5. In any case, in situ inspections are always needed to better check the situation in real time at the warning points under expert geotechnical guidance. 6. An unsupervised learning of the artificial neural network may produce false or missed alarms. One of the purposes of the present monitoring procedure is to reduce both of these. To this purpose, the ability of the monitoring system can be improved  
Figure 11: Map of warnings (the point with the reference behavior is shown in yellow while the warning points are in red). through different actions (e.g., increasing the number of clusters, suitably choosing the training set, and setting the length of the time windows), by exploiting any useful information taken from geological and historical studies in the considered area. In fact, as the monitoring system improves its knowledge, the number of detected anomalous or uncoded behaviors will be reduced. * Despite showing a good ability to detect by similarity anomalous surface movements at their early stage, the procedure was tested in this preliminary study regardless of more strictly geological analyses or in situ inspections. Geological features, which are likely to affect even the choice of the training set, cannot be ignored when applying the monitoring procedure to real cases. * The effectiveness of the proposed monitoring procedure is susceptible to some improvements: (i) considering patterns of geographical points relevant to urban sub-areas either in the training or in the monitoring phase to focus on specific phenomena; (ii) exploiting data relevant to multiple geographical areas to better calibrate the monitoring system; (iii) carrying out in parallel the procedure with different time window lengths and/or scattering thresholds, to forecast different kinds of events. Lastly, some brief notes can be added on the advantages and limitations of the present monitoring system compared to other methods already available in the literature.