## 6 Conclusions  
To provide a more reliable risk mitigation strategy, three commonly applied data analysis approaches were compared in this study to evaluate their suitability for detecting well-known causative co-relations among forcing actions and induced effects over different time window. The OBA was revealed to be more suitable for the detection of cyclical and impulsive events, i.e., those with regular and continuous recurrence over a shorter time window. The SBA was appeared to be more suitable for detecting transgressive series of causing factors and the induced effects, which are cross correlated over a medium to long time window so returning the lag time of influence. The SEA was demonstrated to be powerful for detecting the strain rate variation due to rheological behaviours over long- to very long-time windows. Figure 15: Flow chart illustrating the suitability of the compared approaches for data analysis based on the data recorded by the multi-parametric monitoring system installed at the Acuto test-site with the aim of mitigating the geological risk related to several destabilising actions which induce strain effects on the monitored rock-mass. The continuous-line box indicates the themes and activities carried out in the frame of this research. The dashed-line box indicates the future calibration of an early warning system based on the results of the compared approaches. In particular, for the considered case study of the Acuto test-site (i.e., considering the time of occurrence of destabilising factors here monitored), the temperature was demonstrated to be the main preparatory factor (_sensu_ [PERSON] et al. [12]) in rock mass deformation due to its daily and seasonal oscillation (results obtained mainly by the OBA), which triggered a deformation response of the monitored joints and micro-fractures over a period of 10 min when the daily thermal cycle was considered (results obtained mainly by the SBA). Considering a very long-time window, the daily thermal input involves net strains accumulated in the rock mass due to thermal fatigue phenomena that can lead the rock block towards failure conditions (results obtained by the SEA). Seasonal cycles accumulating over several months, i.e., over a long-time window, result in an inelastic strain which causes a creep-like trend over a multi-season time window. On the contrary, impulsive destabilising actions, such as intense rainfall and windstorms, did not regularly induce strain effects that were objectively linked to causative actions. The results here presented were obtained by considering the size of the Acuto rock block, but the efficiency of the considered approaches can be transposed both to smaller and larger sizing (i.e., by rock sample to rock wall or rock slope size). The approaches discussed herein will be efficiently combined to provide a warning strategy for landslide risk mitigation by stressing their redundancy to: (i) identify precursor signals, so minimising the time for interventions, analysing cause-to-effect relations between forcing actions and strains over short to medium time windows; (ii) compute, through the cross-correlation function, time lags between forcing actions and the resulting strain effects over medium to long time windows; and (iii) detect long-term irreversible strain effects over long- to very long time windows. [PERSON] and [PERSON] conceptualised and designed the multi-parametric monitoring system. [PERSON] performed the field experiments and collected and analysed the monitoring data here presented as results. [PERSON], [PERSON], and [PERSON] supervised and coordinated the research activities. All authors contributed to the interpretation and the discussion of the results and wrote the manuscript. All authors have read and agreed to the published version of the manuscript. This research received no external funding.