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A Rock-fall Early Warning System Based on Logistic Regression Model

Mohammed Abaker1,*, Abdelzahir Abdelmaboud2, Magdi Osman3, Mohammed Alghobiri4, Ahmed Abdelmotlab4

1 Department of Computer Science, College of Science, King Khalid University, Muhayil, 63772, Saudi Arabia
2 Department of Information System, College of Science, King Khalid University, Muhayil, 63772, Saudi Arabia
3 Faculty of Engineering, Department of Electrical Engineering, Dongola University, Dongola, 41129, Sudan
4 Department of Management Information System, College of Business, King Khalid University, Abba, 61421, Saudi Arabia

* Corresponding Author: Mohammed Abaker. Email: email

Intelligent Automation & Soft Computing 2021, 28(3), 843-856. https://doi.org/10.32604/iasc.2021.017714

Abstract

The rock-fall is a natural hazard that results in many economic damages and human losses annually, and thus proactive policies to prevent rock-fall hazard are needed. Such policies require predicting the rock-fall occurrence and deciding to alert the road users at the appropriate time. Thus, this study develops a rock-fall early warning system based on logistic regression model. In this system, the logistic regression model is used to predict the rock-fall occurrence. The decision-making algorithm is used to classify the hazard levels and delivers early warning action. This study adopts two criteria to evaluate the system predictive performance, including overall prediction accuracy measures based on a confusion matrix and the area under a receiver operating characteristic curve (AUC). The results show that the correct prediction accuracy was approximately 79.9%, and the area under the curve (AUC) was 0.85 during the model training. During the validation process, the overall accuracy is 81.0%, and (AUC) is 0.90. The result indicates that this system has high predictive power, strong robustness, and stable performance. That confirms the usefulness of a logistic regression model for predicting a rock-fall occurrence probability.

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APA Style
Abaker, M., Abdelmaboud, A., Osman, M., Alghobiri, M., Abdelmotlab, A. (2021). A rock-fall early warning system based on logistic regression model. Intelligent Automation & Soft Computing, 28(3), 843-856. https://doi.org/10.32604/iasc.2021.017714
Vancouver Style
Abaker M, Abdelmaboud A, Osman M, Alghobiri M, Abdelmotlab A. A rock-fall early warning system based on logistic regression model. Intell Automat Soft Comput . 2021;28(3):843-856 https://doi.org/10.32604/iasc.2021.017714
IEEE Style
M. Abaker, A. Abdelmaboud, M. Osman, M. Alghobiri, and A. Abdelmotlab "A Rock-fall Early Warning System Based on Logistic Regression Model," Intell. Automat. Soft Comput. , vol. 28, no. 3, pp. 843-856. 2021. https://doi.org/10.32604/iasc.2021.017714

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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