# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 6 Conclusions and Future Work  
This study aimed to develop an early warning system in the Kingdom of Saudi Arabia to minimize rock-fall risk along mountain roads. The HEWS system can predict the occurrence of a rock-fall and assess its risk probability, classifying the risk into three levels (unacceptable, tolerable, and acceptable) and delivering a proportional warning action through generating a light alarm signal (red, yellow, and green). This system was \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Period** & **TP FN** & **FP N** & **Sensitivity \%** & **Specificity \%** & **Accuracy \%** \\ \hline
1 & 19 1 & 3 & 17 & 95 & 85 & 90 \\
2 & 18 2 & 1 & 19 & 90 & 95 & 92.5 \\
3 & 17 3 & 3 & 17 & 85 & 85 & 85 \\
4 & 19 1 & 1 & 19 & 95 & 95 & 95 \\
5 & 18 2 & 0 & 20 & 90 & 100 & 95 \\
6 & 16 4 & 1 & 19 & 90 & 95 & 87.5 \\
7 & 17 3 & 0 & 20 & 80 & 100 & 92.5 \\
8 & 18 2 & 3 & 17 & 90 & 85 & 87.5 \\
9 & 18 2 & 2 & 18 & 90 & 90 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: System performance measures (sensitivity, specificity, accuracy). \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Monitoring** & **Prediction** & **Hybrid** \\ \hline Sensitivity & 71.4\% & 88.8\% & 96.7\% \\ Specificity & 86.3\% & 92.2\% & 99.1\% \\ Accuracy & 81.0\% & 90.6 & 97.9\% \\ Reliability & 0.79 & 0.9 & 0.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Overall models performance measures. developed to overcome the limitations of our previous study (32) by increasing the system prediction reliability by combining detection and prediction models in a hybrid reliable early warning system. In order to determine the system's performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix. The results show that the overall system accuracy was 97.9%, and the hybrid model reliability was 0.98, while the previous study's reliability was 0.90. In addition, a system can reduce the risk probability from 6.39 \(\times\) 10\({}^{-3}\) to 1.13 \(\times\) 10\({}^{-8}\). The result indicates that this system is accurate, reliable, and robust, confirming the utility of the proposed system for reducing rock-fall risk. Some limitations still exist in this study. One limitation in the detection model is that it is sensitive to light intensity, causing it to fail to detect and track falling rocks smaller than 49 cm3 under low light conditions. Therefore, further work is required to enhance the detection model by increasing the night lighting intensity on the site and performing an effective frame manipulation before the background subtraction. Furthermore, the proposed method is imperfect in determining the exact moment of the rock-falls, thus future efforts should consider the short-term prediction of rock-fall events. Further work is required to enhance the predictive model by increasing the number of inventory datasets in addition to replacing the current prediction model with a new higher accuracy machine learning model. Conceptualization, A.A. ([PERSON]) and M.A. ([PERSON]); methodology, M.A. ([PERSON]); software, A.A. ([PERSON]); validation, A.A. ([PERSON]), M.A. ([PERSON]) and A.A. ([PERSON]); formal analysis, A.A. ([PERSON]), H.D., M.A. ([PERSON]), M.O. ; resources H.D. ; data curation, M.A. ([PERSON]); writing--original draft preparation, M.A. ([PERSON]); writing--review and editing, A.A. ([PERSON]); visualization, A.A. ([PERSON]); supervision, H.D. ; project administration, M.A. ([PERSON]); funding acquisition, M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through General Research Project under grant number (project/Design and Implementation of Intelligent System for Monitoring and Forecasting Rock Falls to Enhance Traffic Safety/number GRP 110/2019). \"The APC was funded by King Khalid University\". Not applicable.