## 5 Conclusions  
This paper presented a method to monitor ground surface displacements in urban areas with the purpose of forecasting anomalous behaviors from their early occurrence. Through training an SOM neural network, which highlights similarities among recorded timeseries, the method analyzes datasets of MT-InSAR timeseries recorded at hundreds of thousands of geographical points and provides warning signals according to a preset alert criterion. The method applies an unsupervised learning to yield a hierarchical clustering of the training set. An SOM is so obtained, representing the core of the monitoring system, which is able to highlight ground movements preluding the development of critical events. The method was tested on a dataset of InSAR timeseries relevant to a large Italian urban area. By way of example, two different strategies of monitoring were applied, one based on rare behaviors and the other one referring to a recorded noticeable event. The results obtained for both strategies show the capability of the method to highlight ground settlement phenomena at their early stage. This may lead to an automatized system of structural health monitoring in urban areas.  
It should be stressed that, although realistic, the timeseries dataset and the warning criteria here adopted have only an explanatory intent, while the practical application of the method is expected to be carried out with the advice of geotechnical experts. Different critical soil phenomena could be, in fact, put in evidence by the present method, provided that the hyperparameters of the monitoring system are suitably set. Investigating in-depth on such phenomena is, however, outside of the scope of the present study, the main aim of which was to provide a general-purpose tool for the early warning of the foundation-related safety risk of civil structures and infrastructures in urban areas.  
Although being model-independent and broad-spectrum, the present method is expected to be successful even in detecting the starting stage of specific geological phenomena, provided that it is duly calibrated. Calibration of the system and improvement of its selectivity (to avoid false or missed alarms) could be achieved through some strategical actions, for instance, referring to localized patterns of geographical points, considering multiple geographical areas to extract the training set, or carrying out the monitoring system in parallel with different warning criteria and hyperparameters. These issues will be addressed in future lines of research.  
Conceptualization, A.M. and M.C.P.; data curation, A.M. and M.C.P.; investigation, A.M. and M.C.P.; methodology, A.M. and M.C.P.; software, A.M.; supervision, M.C.P.; validation, A.M. and M.C.P.; writing--original draft, A.M. and M.C.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability Statement: Data are available on request.  
The Globus Mobility Program of the University of Cagliari (Italy) is acknowledged. [PERSON] (University of Houston, Texas) is gratefully acknowledged for his useful insights and for kindly providing the MT-InSAR data used in the study.  
The authors declare no conflict of interest.