| 1 |
What is the purpose of the empirical case study on coal manufacturing in the paper?
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To demonstrate the proposed decision support model |
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| 2 |
Which factor does the model NOT consider in route selection for a multimodal transportation network?
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Cultural preferences |
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| 3 |
What is the role of expert judgments in the decision support model?
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They define the ZOGP objective function |
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| 4 |
What logistics system aspect does the proposed methodology aim to improve?
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Marketing strategies |
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| 5 |
Essay | Describe the role of Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP) in the decision support model for determining an optimal multimodal transportation route. Explain how these methodologies contribute to the model's effectiveness and discuss any potential limitations.
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Describe the role of Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP) in the decision support model for determining an optimal multimodal transportation route. Explain how these methodologies contribute to the model's effectiveness and discuss any potential limitations. |
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| 6 |
What is the role of the FAHP method in the proposed risk analysis model?
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| 7 |
Which industry is used as a case study in the proposed risk analysis model?
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Technology |
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| 8 |
What does the DEA method do in the proposed FAHP-DEA methodology?
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Evaluates linguistic variables and generates risk scores |
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| 9 |
Which method is used to aggregate risk scores into an overall risk score in the proposed model?
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Monte Carlo Simulation |
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| 10 |
Essay | Using the coal industry case study, please explain how the proposed risk analysis model is practical and aids in prioritizing risks. Discuss how this model can be beneficial for industries in optimizing multimodal transportation routes under risk decision criteria.
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Using the coal industry case study, please explain how the proposed risk analysis model is practical and aids in prioritizing risks. Discuss how this model can be beneficial for industries in optimizing multimodal transportation routes under risk decision criteria. |
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| 11 |
How were geotechnical parameters of soils at landslide-prone sites evaluated in the study?
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| 12 |
What modeling techniques were used to assess the probability of landslide occurrence in the future?
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Autoregressive Moving Average (ARIMA) model |
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| 13 |
What is the potential application of the study's findings in hazard management?
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Heavy trafficPromoting tourism in landslide-prone areas |
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| 14 |
How does the study aim to contribute to hazard management in the Himalayas?
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By serving as a guiding framework for using artificial intelligence and machine learning |
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| 15 |
Essay | Explain the methodology employed in the study to evaluate geotechnical parameters and assess the probability of future landslide events. Discuss the potential implications of using artificial intelligence and machine learning in hazard management in the Himalayas, with reference to the study's guiding framework.
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Explain the methodology employed in the study to evaluate geotechnical parameters and assess the probability of future landslide events. Discuss the potential implications of using artificial intelligence and machine learning in hazard management in the Himalayas, with reference to the study's guiding framework. |
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| 16 |
How was the landslide inventory database divided for training and testing in the research?
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60% training, 40% testing |
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| 17 |
Which machine learning model showed the highest prediction rate among LR, RF, and DRT?
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Logistic Regression (LR) |
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| 18 |
What do the ROC values for training and testing data signify in the context of landslide susceptibility mapping?
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The accuracy of the machine learning models |
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| 19 |
Which model is considered more realistic according to susceptibility zones in the research?
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Decision and Regression Tree (DRT) |
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| 20 |
Essay | Compare and contrast the performance of Logistic Regression (LR), Random Forest (RF), and Decision and Regression Tree (DRT) models in landslide susceptibility mapping. Discuss the strengths and limitations of each model based on the research findings.
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Compare and contrast the performance of Logistic Regression (LR), Random Forest (RF), and Decision and Regression Tree (DRT) models in landslide susceptibility mapping. Discuss the strengths and limitations of each model based on the research findings. |
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