# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
## 6 Conclusions  
In this study, an early warning framework was developed to reduce the risk of rock falls. First, 2040 samples of historical rock-fall event data were gathered from various sources, and randomly split into two groups, with 70% going for deep learning model training and 30% going for model validation. Next, the model prediction accuracy was augmented using a real-time rock-fall detection model based on IoT and computer vision. Finally, the decision-making algorithm assesses the risk of a rock fall, categorizes it into three levels, and produces a warning reaction to handle the critical hazard situation.  
This study utilized parameters and overall prediction performance measures based on a confusion matrix to compare the performance of the model before and after augmentation. The results showed that the models had acceptable performance. The results show that the overall model accuracy before augmentation was 86%, which became 98.8% after the augmentation. Additionally, as shown in Table 9, a framework can lower the risk probability from \(1.51\times 10^{-3}\) to \(8.57\times 10^{-9}\).  
By comparing our new augmented model with our model in a previous study [14] based on performance and the ability to reduce the risk of rock fall, we discovered that the new proposed model's prediction accuracy was 98.8%. In comparison, the previous study's accuracy was 97.9%. In addition, the new model reduced the risk probability to (\(8.57\times 10^{-9}\)), while the previous study reduced the risk to (\(1.13\times 10^{-8}\)). This result indicates that although the previous model had acceptable performance, the augmented model outperformed it. It can be considered a promising technique for predicting and reducing rock-fall risks.  
In future studies, we suggest enhancing the real-time rock-fall detection process by increasing the number of seismic sensors to achieve a higher detection accuracy and enhancing the current DL prediction model's accuracy by adding more rock-fall conditioning factors.  
Conceptualization, M.A. and H.D.; methodology, M.A.; software, H.D.; validation, M.A., H.D. and T.A.E.E.; formal analysis, M.A.; investigation, M.B.; resources, H.A. and M.I.A.; data curation, A.H.; writing--original draft preparation, M.A.; writing--review and editing, H.D. and A.M.; visualization, M.A.M.; supervision, M.A.; project administration, M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Group (Project under grant number RGP.2/320/44).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline
**Rock-Fall Risk Probability** & **Minimum** & **Maximum** \\ \hline Before Reduction & \(7.98\times 10^{-6}\) & \(1.51\times 10^{-3}\) \\ \hline After Reduction & \(8.57\times 10^{-9}\) & \(8.21\times 10^{-7}\) \\ \hline \end{tabular}
\end{table}
Table 9: Rock-fall risk probability before and after reduction.