## 4 Results
### Risk Estimation of New Hazard Events by the Trained Network
#### 4.4.3 The Similarity between the New Hazard Events and the Existing Ones  
As indicated in Section 4.4.1, the trained network demonstrated robust stability in estimating risks associated with new hazard events similar to the existing ones. However, it was observed that for certain dissimilar hazard events, as discussed in Section 4.4.2, there were large deviations observed in risk estimation with a specific probability. Therefore, assessing the degree of similarity between a new hazard event and existing ones is crucial for effectively utilizing the LSTM-based risk estimation methods. In this paper, similarities between the new hazard events and the ones in the training dataset were investigated by the cosine similarity method, a mathematical method using the similarity between texts as in Equation (12), derived from the calculus of vectors in mathematics [37]. \[\cos(\theta)=\frac{\sum(a_{i}b_{i})}{\sqrt{a_{i}^{2}\sqrt{b_{i}^{2}}}} \tag{12}\]  
where \(a_{i}\) and \(b_{i}\) represent the vector values of the two texts (hazardous situation descriptions), respectively. Scores close to one indicate strong similarity; scores close to zero indicate a weak similarity. The heat map in Figure 15 illustrates the similarities between the new hazard events and the training dataset in the first test run of experiment C, while Figure 16 presents the similarities between the new hazard events and the training dataset in the first test run of experiment D. The horizontal axis in the heatmap represents the number of hazard events in the training dataset, with augmented hazard events originating from the same source positioned adjacent to each other. The vertical axis represents the number of the new hazard events, and the color depth represents the similarity between the new hazard event and the No.i (\(1\leq i\leq 651\)) hazard event in the training dataset. Comparing these two  
Figure 14: Results of risk estimation on the new hazard events in experiment D.  
figures, it is evident that in experiment C, the new hazard events can readily identify their similar ones--represented by dark-clustered color blocks. Conversely, in experiment D, the new hazard events face difficulty in doing so. Based on the results of experiment C, it can be inferred that the risk index of new hazard events primarily depends on the risk index of highly similar hazard events in the training dataset. In experiment C, No.583 hazard event in the 2 nd test run, No.505 hazard event in the 5 th test run, No.6 hazard event in 1 the 7 th test run and No.261 hazard event in the 26 th test run were large deviations. Similarities between these new hazard events and their corresponding training dataset are shown in Figure 17. There were two dark-clustered blocks in Figure 17a,b, while there was a single wide dark-sparse block in Figure 17c,d, which implied that multiple existing hazard events in the training dataset exhibited similarities to the new hazard event. Figure 16: Heatmap of similarities between the new hazard event dataset and the training dataset in the first test run of experiment D.  
Figure 15: Heatmap of similarities between the new hazard event dataset and the training dataset in the first test run of experiment C.  
Figure 17: _Cont._  
The details of the blocks in Figure 17a were enumerated in Table 4 to conduct a more comprehensive investigation into the underlying causes of risk estimation deviation. The true risk index of No.583 was RI 5, but the estimated risk index by the trained network was RI 2. Although No.583 had a high similarity to No.584-No.588, which originated from the same raw hazard event with a risk index of 5, it also demonstrated certain similarities to No.50-No.61, originating from different raw hazard events with a risk index of 2. Therefore, in cases where multiple events with low similarity to the new hazard event are present in the training set, their cumulative impact may surpass or equal that of high similarity events, leading to a deviation in risk estimation.