A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems

Kaewfak, Kwanjira; Huynh, Van–Nam; Ammarapala, Veeris; Ratisoontorn, Nantaporn

doi:10.1109/access.2020.3018669

Cited by 27 publications

(14 citation statements)

References 49 publications

(103 reference statements)

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“…While qualitative approaches may not be ideal, the development of an integrated model for improving qualitative risk management is therefore vital. In this regard, methods like multiple criteria decision making (MCDM) have been employed in various risk and uncertainty contexts, which are based on the process of risk management [25,26].…”

Section: A Risk Management and Methodologiesmentioning

confidence: 99%

An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management

2021

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An integrated method comprising Data Envelopment Analysis (DEA) and machine learning (ML) for risk management is proposed in this paper. Initially, in the process of risk assessment, the DEA cross-efficiency method is used to evaluate a set of risk factors obtained from the Failure Mode and Effect Analysis (FMEA). This FMEA-DEA cross-efficiency method not only overcomes some drawbacks of FMEA, but also eliminates several limitations of DEA to offer a high discrimination capability of decision units. For risk treatment and monitoring processes, an ML mechanism is utilized to predict the degree of remaining risk depending on simulated data corresponding to the risk treatment scenario. Prediction using ML is more accurate since the predictive power of this model is better than that of DEA which potentially contains errors. Based on a case study with a group of logistics service providers, the results ascertain that the combined DEA and ML approach offers a flexible and reasonable alternative in risk management. The approach allow decision-makers or managers to assess and monitor the risk level for handling forthcoming events in unusual conditions. It also serves as a useful knowledge repository such that appropriate risk mitigation strategies can be planned, along with the predicted results. The outcome of our empirical evaluation indicates that the proposed approach contributes towards robustness in sustainable business operations.

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Section: A Risk Management and Methodologiesmentioning

confidence: 99%

An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management

2021

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“…Then, the risk was calculated in terms of the temporal and spatial information on the effect of precipitation. Based on the possibility that cars are available in a certain position and time period affected by rocks falling, the temporalspatial probability and susceptibility were determined [34]. Equation (12) shows the risk probability's value.…”

Section: Rock-fall Risk Assessmentmentioning

confidence: 99%

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

Abaker,

Dafaalla,

Eisa

et al. 2023

Applied Sciences

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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.

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“…The risk was calculated by integrating the likelihood that the fallen rocks will hit the vehicle, the temporal-spatial probability, and the susceptibility. In this paper, the likelihood of rocks falling is calculated based on the possibility that vehicles are presenting a specific position and period affected by rocks falling [42]. The rock-fall risk is determined utilizing a probability equation that combines the probability of the occurrence, the temporal and the spatial probabilities, and the vulnerability.…”

Section: Rock-fall Risk Assessmentmentioning

confidence: 99%

Hybrid Early Warning System for Rock-Fall Risks Reduction

et al. 2021

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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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A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems

Cited by 27 publications

References 49 publications

An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management

An Application of Data Envelopment Analysis and Machine Learning Approach to Risk Management

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

Hybrid Early Warning System for Rock-Fall Risks Reduction

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