## 3 Results
### Bibliometric Analysis Results for Sensors Monitoring Mine Tailings Storage Facilities
#### 3.2.4 Keyword Co-Occurrence Analysis  
The bibliometric metadata obtained from the search for the 52 selected articles were processed in VOSviewer to produce a co-occurrence map without considering the timescale. The minimum occurrence value was two keywords; in other words, a keyword appears on the map when two articles use it. The resulting map shows four clusters, which are represented in green, blue, red, and yellow in Figure 11.  
In the co-occurrence map in Figure 11, four clusters appear, which are represented by different colors. Among the keywords, the most prominent are \"monitoring\", \"tailings dam\", and \"safety\". Table 3 presents an interpretive summary of Figure 11, clarifying the themes of each cluster and the prominent keywords identified through the co-occurrence analysis.  
According to the cluster interpretations presented in Table 3, the development of sensor applications strongly trends toward their use in TSFs for monitoring, safety management, and the development of advances in sustainability monitoring.  
The bibliometric metadata resulting from the search for the 52 selected articles were processed in VOSviewer to produce a co-occurrence map considering the timescale as well. The resulting map shows three clusters (represented in green, blue, and yellow) by publication year (Figure 12).  
In the co-occurrence map presented in Figure 12, the timescale on which these concepts were generated can be appreciated; the most recent topics are risk management, remote sensing, and deep learning, while the oldest include disasters, alarm systems, and dam deformation. Table 4 presents an interpretive summary of Figure 12 to elucidate the themes that appear by cluster and the prominent keywords identified by the co-occurrence analysis.  
The cluster interpretation presented in Table 4 shows the evolution over time of the application of sensors for monitoring safety trends, starting with issues of accidents and disasters, then evolving to consider the development of means of powerful wireless monitoring systems, and, finally, the development of technologies to support the total digitalization of information.  
Figure 10: Distribution of documents by type.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster Identification** & **Keywords from Co-Occurrence Analysis** & **Cluster Interpretation** \\ \hline Yellow & On-line monitoring, safety engineering, failure & Monitoring \\ Blue & Early warning, tailings pond, accidents & Safety management \\ Red & Real-time monitoring, alarm systems, dam deformation & Wireless sensor networks \\ Green & Deep learning, remote sensing, digital storage & Digital technologies \\ \hline \hline \end{tabular}
\end{table}
Table 3: VOSviewer clusters without the time dimension.  
Figure 11: Keyword co-occurrence map without the time dimension.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cluster** & **Keywords** & **Average Year of Publication** & **Cluster Interpretation** \\ \hline Blue & Safety management, alarm systems, dam deformation, water levels. & 2014–2016 & Prevention of accidents and disasters \\ \hline Green & Real-time monitoring, online monitoring, safety engineering & 2016–2018 & Advances in monitoring systems \\ \hline Yellow & Risk management, deep learning, remote sensing, digital storage & 2018–2020 & Digital monitoring with the use of Industry 4.0 technologies \\ \hline \hline \end{tabular}
\end{table}
Table 4: VOSviewer clusters with the timescale dimension.  
Figure 12: Keyword co-occurrence map with the time dimension.