# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 4 Results and Discussion
### Collaborative Institution Network Analysis
The thicker the purple rings, the higher the betweenness centrality of the keywords. A high betweenness centrality score signifies that the associated keyword acts as a crucial intermediary in connecting all co-occurring keywords within the keyword co-occurrence network. The top five keywords with the highest betweenness centrality are displayed in Table 11. The keywords \"extreme event\" and \"el_nino\" with higher publication frequency also hold the highest betweenness centrality, degree centrality as well as closeness centrality, revealing that predicting extreme events, especially weather-related events (e.g., \"el_nino\" and \"extreme weather event\") have been hot research focus and of great significance in the event prediction field during the past decade and play important roles in the partial network as well as the entire network. Predicting extreme weather events requires monitoring weather data such as atmospheric conditions, temperature, humidity, etc., which is usually collected through weather sensors and weather stations. The El Nino prediction usually requires monitoring climate and meteorological indicators such as sea surface temperature from satellites and ocean sensors. Evaluating the performance of prediction models and methods with accuracy is also a concern in the event prediction field. Table 12 presents the top five keywords with the strongest citation bursts. A stronger citation burst is indicated by a higher strength score. The term \"recurrent neural network\" exhibits the most robust citation burst, with the highest score of 13.32 during 2017 and 2022. This is because recurrent neural network as a typical type of deep learning method has been widely used for event prediction in multidisciplinary fields, aiming at improving the performance of prediction models with advanced techniques. The \"extreme event\" with a citation burst of 11.32 has received an increase in attention since 2018. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Keywords** & **Betweenness Centrality** & **Degree Centrality** & **Closeness Centrality** & **Year** \\ \hline Extreme event & 0.23 & 87 & 0.86 & 2012 \\ El Nino & 0.16 & 64 & 0.77 & 2013 \\ Sea surface temperature & 0.13 & 21 & 0.68 & 2012 \\ Extreme weather event & 0.10 & 29 & 0.73 & 2016 \\ Prediction accuracy & 0.08 & 38 & 0.69 & 2012 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: The top five keywords sorted by the betweenness centrality. Figure 11: The keyword co-occurrence network for years 2012 to 2022. Sensors have usually provided effective data sources for monitoring and predicting abnormal situations (i.e., events) in multiple fields since 2014, especially in dealing with weather event prediction. For instance, weather sensor data can be imported into models such as recurrent neural networks to capture the association between meteorological conditions and extreme weather events. Another keyword cited with a significant increase in the most recent years is \"artificial intelligence\", of which the citation burst score is 9.75. It is a trend that the large-scale collection and processing of sensor data require powerful computing power and intelligent algorithms. Furthermore, we constructed a two-dimensional keyword timeline graph in Figure 12, which arranges the keyword co-occurrence network and the corresponding cluster analysis results in chronological order, so as to investigate the distribution of research topics in the event prediction field and how they evolve over time. The horizontal axis on the top signifies time. Each horizontal line denotes a cluster. The clusters are ordered according to the earliest occurrence time of the documents in each cluster. The circle located on the horizontal line represents the keywords in the cluster, and the position of the circle is the time when the keyword first emerged in the cluster. The concentric rings around each keyword illustrate the time range during which the keyword appeared. The labels of the clusters were generated by the LLR method.