Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning

• Published in: Applied sciences 2023-09, Vol.13 (17), p.9978
• Main Authors: Abaker, Mohammed, Dafaalla, Hatim, Eisa, Taiseer Abdalla Elfadil, Abdelgader, Heba, Mohammed, Ahmed, Burhanur, Mohammed, Hasabelrsoul, Aiman, Alfakey, Mohammed Ibrahim, Morsi, Mohammed Abdelghader
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial intelligence; Cameras; Datasets; Decision making; Deep learning; early warning; Energy consumption; Falls; Internet of Things; Internet of Things IoT; Machine learning; Monitoring systems; Rain; Regression analysis; rock-fall risk; Sensors; Temperature
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13179978

In recent years, several strategies have been introduced to enhance early warning systems and lower the risk of rock-falls. In this regard, this paper introduces a deep learning- and IoT-based framework for rock-fall early warning, devoted to reducing rock-fall risk with high accuracy. In this framework, the prediction accuracy was augmented by eliminating the uncertainties and confusion plaguing the prediction model. In order to achieve augmented prediction accuracy, this framework fused prediction model-based deep learning with a detection model-based Internet of Things. This study utilized parameters, namely, overall prediction performance measures based on a confusion matrix, to assess the performance of the framework in addition to its ability to reduce the risk. The result indicates an increase in prediction model accuracy from 86% to 98.8%. In addition, the framework reduced the risk probability from 1.51 × 10−3 to 8.57 × 10−9. Our findings demonstrate the high prediction accuracy of the framework, which also offers a reliable decision-making mechanism for providing early warning and reducing the potential hazards of rock falls.