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Hybrid Early Warning System for Rock-Fall Risks Reduction

Abdelzahir Abdelmaboud,Mohammed Abaker,M. Osman,Mohammed Alghobiri,Ahmed Abdelmotlab,Hatim Dafaalla

Published 2021 in Applied Sciences

ABSTRACT

Rock-fall is a natural threat resulting in many annual economic costs and human casualties. Constructive measures including detection or prediction of rock-fall and warning road users at the appropriate time are required to prevent or reduce the risk. This article presents a hybrid early warning system (HEWS) to reduce the rock-fall risks. In this system, the computer vision model is used to detect and track falling rocks, and the logistic regression model is used to predict the rock-fall occurrence. In addition, the hybrid risk reduction model is used to classify the hazard levels and delivers early warning action. In order to determine the system’s performance, this study adopted parameters, namely overall prediction performance measures, based on a confusion matrix and reliability. The results show that the overall system accuracy was 97.9%, and the reliability was 0.98. In addition, a system can reduce the risk probability from (6.39 × 10−3) to (1.13 × 10−8). The result indicates that this system is accurate, reliable, and robust; this confirms the purpose of the HEWS to reduce rock-fall risk.

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