# Sudden Event Prediction Based on Event Knowledge Graph
## 4 Methods
### Event Knowledge Graph Construction--Taking the Traffic Domain as an Example
For this reason, we adjusted the prior value \(\lambda\) to change the accurate matching value and observed the changes in other evaluation metrics, as shown in Figure 6. Initially \(\lambda\) was one while the accuracy was only around 0.15, the false positive rate was extremely high and the model made a large number of predictions to cover the true labels. The recall value kept decreasing as \(\lambda\) increased. The precision value kept increasing when \(\lambda\) was lower than 0.58. The model predicted some of the positive cases as negative cases while correcting some of the false positive cases, and predicted these false positive cases as negative cases. This means that for some uncertain events, the model preferred to predict that they would not happen. As far as the experimental results are concerned, GCN outperformed GAT, while the model in this paper had a significant improvement over GAT and outperformed GCN, reflecting the role of the heterogeneous attention. This indicates that different arguments and neighboring events have different effects on events, and fusing them with different weights can improve the machine's ability to perceive situational information. ### Ablation Study

In this section, we set up ablation experiments to investigate the effect of different event representations on the prediction results. This included ablating relations and arguments from the event graph. The relation-level ablation experiments removed concurrent event relation edges from the graph and kept only the arguments. In the argument-level ablation experiments, the temporal and spatial parameters of events were removed, while other parameters and the graph structure were retained. The results of the ablation experiments are shown in Table 3.

It can be seen from the experimental results that the performance of the model decreased to some extent in different ablation experiments. Notably, there was a larger increase in recall values in the relation-level ablation experiments, which indicated that although the inclusion of concurrent events effectively improved the prediction accuracy, it was also an important factor in making the model predictions more conservative. In the argument-level ablation experiments, the decrease in metrics reflected the positive effect of temporal and spatial information on the event prediction task. ### Case Study

In order to show the prediction results of our model more intuitively, we selected some representative cases. Our model had the ability to accurately predict multiple outcomes of events, and we analyzed the reasons why some events were not accurately predicted. Case 1. 16 December 2019 afternoon A bus carrying 42 passengers from the capital Khartoum to Maidani was involved in a major traffic accident near the town of Abu Osher. Case 2. Xinhua News Agency, Rio de Janeiro, September 20 (Reporter [PERSON]) Minas Gerais state fire department in southeastern Brazil said on September 20, the state early that morning, a small bus and truck collision. Case 3. A collision between an off-road vehicle and a container truck occurred in the early morning of 11 in Agra district, Uttar Pradesh, India. Our model in Case 1 predicted \"fatalities\" and \"large injuries (serious injury and many injured)\" based on the arguments: 16 December 2019 afternoon, Abu Osher town, bus, occurrence, traffic accident. The follow-up result \"2 people died on the spot, 12 people were seriously injured, and other passengers were injured to varying degrees\" also included \"fatal\" and \"major injuries\" events. Our forecast was consistent with this. Case 2 follow-up events were \"death\", \"minor injuries (minor injury and few injured)\", and \"accident investigation\". Case 3 follow-up events were \"death\", \"minor injury\", and \"injury treatment\". Our model predicted only \"death\" and \"minor injury\", which was only partially correct.