# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 5 Results and Discussion
### Hybrid Risk Reduction Model Results  
After data were collected from the historical data of the rock-fall accidents and the traffic flow data set, we used a Python environment to simulate the rock-fall risk reduction. The hybrid model obtained it and compared the result with the obtained reduction using the detection and the prediction models separately. The configurations and the setups used for simulation are shown in Table 5.  
The simulation results in Figure 12 show the effect of using the three models, monitoring, prediction, and hybrid models, separately to reduce the risk. We found that the risk probability varied from 4.26 \(\times\) 10\({}^{-8}\) to 1.44 \(\times\) 10\({}^{-7}\) with an average value of 8.62 \(\times\) 10\({}^{-8}\). When using the prediction model only, the result was represented by the blue curve. Then, when using the monitoring model only, the result was represented by the green curve. We found that the risk probability varied from 2.35 \(\times\) 10\({}^{-8}\) to 2.01 \(\times\) 10\({}^{-7}\) with an average value of 1.27 \(\times\) 10\({}^{-7}\). Finally, when using a hybrid model only, the result was represented by the red curve. We found that the risk probability varied from 3.38 \(\times\) 10\({}^{-9}\) to 1.88 \(\times\) 10\({}^{-8}\) with an average value of 1.13 \(\times\) 10\({}^{-8}\).  
Table 6 summarizes the highest and the lowest risk probabilities after reduction for the three models in addition to the average risk for each model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Monitoring** & **Prediction** & **Hybrid** \\ \hline Lowest & 4.26 \(\times\) 10\({}^{-8}\) & 2.35 \(\times\) 10\({}^{-8}\) & 3.38 \(\times\) 10\({}^{-9}\) \\ Highest & 1.44 \(\times\) 10\({}^{-7}\) & 2.01 \(\times\) 10\({}^{-7}\) & 1.88 \(\times\) 10\({}^{-8}\) \\ Average & 8.62 \(\times\) 10\({}^{-8}\) & 1.27 \(\times\) 10\({}^{-7}\) & 1.13 \(\times\) 10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of risk probability after reduction.  
Figure 12: Rock-fall risk probability after being reduced by the system models.  
By comparing the risk curves of the three models, we found that, in the case of the monitoring model, the risk probability was low between 06:00 and 18:00 and high before and after this period due to the camera's response to sunlight and the device's lighting at night. In a prediction model, the risk probability was high between 0:700 and 21:00 and low before and after this period due to the traffic density on the road during this period. In a hybrid model, the risk probability curve was semi-linear due to the increase in model reliability, which was gained from a parallel combination of the detection and the prediction models' reliabilities, as mentioned in Equation (6).  
In another way, the model acquired the linearity from the result of mutual compensation by the detection and the adjustment models for each other's shortcomings. For the monitoring model, it reported an absent event as present or reported the occurrence event as absent. The prediction model corrected this situation by confirming occurrence or no occurrence of the event at this moment. In the same way, the monitoring model corrected the confusing cases of a prediction model by confirming occurrence or no occurrence of the event at this moment.  
By comparing the measured risk probability after reduction, as in Table 6, with the triangle of ALARP thresholds in Figure 12, we found that the risk values were situated in an area that was usually acceptable.