Hybrid Early Warning System for Rock-Fall Risks Reduction

Applied Sciences

Hatim Dafaalla¹  Mohammed Abaker¹  Abdelzahir Abdelmaboud²  Magdi Osman³  Ahmed Abdelmotlab⁴  Mohammed Alghobiri⁴ 

[1] Department Computer Science, Community College, King Khalid University, Muhayel Aseer 61913, Saudi Arabia;Department of Information System, College of Science and Art, King Khalid University, Muhayel Aseer 61913, Saudi Arabia;Electrical Engineering Department, College of Engineering, Dongola University, Dongola 44411, Sudan;Management Information System Department, College of Business, King Khalid University, Abha 61421, Saudi Arabia;

关键词: detection;    prediction;    risk reduction;    rock-fall;   

DOI  :  10.3390/app11209506

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown