Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
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Published:2023-09-04
Issue:17
Volume:13
Page:9978
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Abaker Mohammed1ORCID, Dafaalla Hatim1ORCID, Eisa Taiseer Abdalla Elfadil2, Abdelgader Heba2, Mohammed Ahmed3, Burhanur Mohammed3, Hasabelrsoul Aiman4, Alfakey Mohammed Ibrahim5, Morsi Mohammed Abdelghader6
Affiliation:
1. Department of Computer Science, Applied College, King Khalid University, Muhayil 61913, Saudi Arabia 2. Department of Information System, College of Science and Art, King Khalid University, Muhayel 61913, Saudi Arabia 3. Department of Information System, Applied College, King Khalid University, Muhayil 61913, Saudi Arabia 4. Department of Business Administration, Applied College, King Khalid University, Muhayel 61913, Saudi Arabia 5. Department of Computer Science, College of Science and Art, King Khalid University, Tanumah 62711, Saudi Arabia 6. Department of Computer Science, Jordanian Sudanese College for Science and Technology, Khartoum 12217, Sudan
Abstract
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.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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