## 3 Experiments and Results
### Comparison of Hybrid Application Results and Similarity Matching Results  
To support emergency decision making, the trained decision model uses the vector of an oil spill scenario as the inputs and outputs the Q value corresponding to each action. Generally, the higher the Q value is, the greater the probability of taking the corresponding action that the model suggests to the decision maker. To verify the feasibility of the proposed method, four typical marine oil spill scenarios (five scenario instances) are selected in this section, as shown in Table 4. Using these five scenario instance vectors as inputs, the outputs of the state-action value curve are shown in Figure 4.  
Figure 4a-d illustrates the Q value of the emergency response action by applying the trained model under four typical scenarios: tanker collision, tanker caught fire, oil spill and marine organism death. Figure 4a shows an oil tanker collision scenario instance under normal sea conditions. The optimal emergency response action suggested by the model is \"use of mechanical recycling and sorbent materials\". From the results, the Q value of the optimal action is not far from the Q value of other emergency response actions, including the \"use of booms\" and the \"use of dispersants\". Additionally, in such a tanker collision scenario, all potential actions can be implemented at once. Figure 4b shows an example of a tanker fire scenario under normal sea conditions. The best recommendation given by the model is \"extinguishing the fire\", and the Q value for selecting a firefighting emergency response action is much higher than that of other emergency response actions. This recommendation is consistent with the actions taken for the SEA STAR accident. In the historical case of the SEA STAR, the oil tanker exploded during recovery without extinguishing the fire, which led to the ship sinking in the Gulf of Oman. Figure 4c shows the results for two oil spill scenarios. When the sea conditions are normal, various methods for remediating spilled oil are recommended. Moreover, only \"mechanical recycling\" is recommended under rough sea conditions because oil booms lose efficacy under high wave conditions and dispersants are ineffective in low-temperature water. However, in the case of rough sea conditions, the optimal emergency response action given by the model is \"stopping ship \begin{table}
\begin{tabular}{c c c} \hline \hline
**Order** & **Typical Scenario** & **Scenario Instance** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{Tanker collision scenario} & Set spilled oil amount. All other parameters are set as default values. Sea condition parameter values set to “normal”. Scenario instance extracted from the case “ATLANTIC EMPRESS”. \\  & & & Set spilled oil amount, where the spilled oil is \\  & & & flammable. All other parameters are set as default values. Sea condition parameter values set to “normal”. Scenario instance extracted from the case “ATLANTIC EMPRESS”. \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Oil spill scenario} & Set spilled oil amount. Sea condition parameter values set to “normal”. Scenario instance extracted from the case “BRAER”. \\  & & & Set spilled oil amount. Sea condition parameter values set to “dangerous”. Scenario instance extracted from the case “TANIO”. \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{Marine organism death scenario} & Assume the spilled oil has been cleaned up. \\  & & Sea condition parameter values set to “normal”. \\ \cline{1-1}  & & Scenario instance extracted from the case “BRAER”. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Four typical scenarios (with five scenario instances) in the experiment. leaks\", with the Q value of the action being much higher than that for other actions, which seems unreasonable. Therefore, it is essential to further optimize the values of the indicators used to assess scenarios in the future. Figure 4d represents a biological impairment scenario in marine environments that leads to organism death.