| 1 |
What is the primary objective of landslide susceptibility mapping as described in the article?
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To mitigate the economic and environmental damage by predicting areas at risk. |
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The primary objective of landslide susceptibility mapping is to mitigate the economic and environmental damage by predicting areas at risk. Rather than forecasting the exact timing of a landslide, this method helps identify where landslides are most likely to occur, allowing for better planning, construction, and disaster preparedness. |
According to Guzzetti et al. (1999) and Van Westen et al. (2006), landslide susceptibility mapping uses historical landslide data, terrain features, and geotechnical information to assess slope stability and generate risk maps. These maps are essential tools for minimizing damage through informed land-use decisions and early warning systems. |
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| 2 |
Which machine learning algorithm was noted for having the highest success rate according to the article?
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Random Forest |
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According to the article, the Random Forest algorithm showed the highest success rate in predicting or classifying landslide occurrences due to its strong ability to handle nonlinear data, reduce overfitting, and manage large datasets with many input variables. |
Breiman (2001) introduced Random Forest as an ensemble learning method that builds multiple decision trees and combines their results for better accuracy and stability. |
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| 3 |
If the area of Chattogram district is 75% susceptible to landslides, and the highly susceptible zone covers approximately 12% of the district, what is the area (in percentage) that is not highly susceptible?
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87% |
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Not highly susceptible = 100% − 12% = 88% |
Not highly susceptible area (%) = 100% − Highly susceptible area (%) |
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| 4 |
Considering that the total number of analyzed landslides is 255, and 80% were used for training the models, how many landslide instances were used for testing?
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51 |
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Testing instances = 20% of 255
= 0.20 × 255
= 51 instances |
Testing instances = Total instances × (Percentage used for testing ÷ 100) |
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| 5 |
If the total area of Chattogram district is 7,000 km² and the very high susceptible zone covers 9% of the district, what is the area of the very high susceptible zone in km²?
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630 km² |
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Area = (9 / 100) × 7000 = 630 km² |
Area = (Percentage / 100) × Total area |
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| 6 |
Assuming the false positive rate (FPR) for the logistic regression model is 0.05 and the true positive rate (TPR) is 0.95, calculate the specificity of the model.
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0.95 |
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Specificity = 1 − 0.05 = 0.95 |
Specificity = 1 − FPR |
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| 7 |
Given that the area under the ROC curve (AUC) for the logistic regression model is 0.963, and the prediction rate is measured as the area under this curve, rate the model's prediction accuracy.
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Excellent |
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Since the AUC is 0.963, the model’s prediction accuracy is considered excellent. |
The area under the ROC curve (AUC) measures how well a model distinguishes between classes:
• 0.5 – 0.6 = Poor
• 0.6 – 0.7 = Below average
• 0.7 – 0.8 = Average
• 0.8 – 0.9 = Good
• 0.9 – 1.0 = Excellent |
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| 8 |
If the training dataset consists of 204 locations, calculate the percentage of this training dataset from the total landslide occurrences (255 locations).
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80% |
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(204 / 255) × 100 = 80% |
Percentage = (Training data / Total data) × 100 |
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| 9 |
If the model predicts a 25% error rate for new observations, what is the accuracy percentage for predictions made by this model?
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75% |
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Accuracy = 100% − 25% = 75% |
Accuracy (%) = 100% − Error rate (%) |
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| 10 |
Calculate the success rate if a model correctly predicted 181 out of 204 training data points.
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88.73% |
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(181 / 204) × 100 ≈ 88.73% |
Success rate (%) = (Number of correct predictions / Total training data) × 100 |
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| 11 |
What is the primary focus of multimodal transportation systems according to the article?
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Enhancing environmental sustainability and safety. |
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Multimodal transportation systems aim not only to improve efficiency and reduce costs but also to reduce environmental impact and increase safety by using the best transport modes for each segment of a journey. |
Rodrigue & Notteboom (2020), modern multimodal systems emphasize environmental sustainability by shifting freight to less polluting modes like rail and waterways. |
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| 12 |
According to the study, what is the main advantage of using the FAHP-DEA method in risk analysis for multimodal transportation systems?
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It allows for precise risk prioritization and optimization of routes. |
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FAHP-DEA combines expert judgments with efficiency analysis, helping to prioritize risks accurately and optimize transportation routes effectively. |
research by Kuo and Smith (2010), combining these methods provides a more comprehensive decision-making tool, improving both risk assessment accuracy and operational efficiency in complex transportation systems. |
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| 13 |
If the risk analysis model has five criteria and assigns importance weights such that the total sums up to 1, and the weights for operational risk and security risk are 0.157 and 0.073 respectively, what is the combined weight of the remaining three criteria?
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0.770 |
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= 1 − (0.157 + 0.073) = 1 − 0.23 = 0.77 |
Weight of remaining criteria = 1 − (sum of weights of known criteria) |
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| 14 |
If the probability of an accident occurring on a route is 0.2 and the consequence severity is rated at 0.5, what is the risk level for that route segment using the model
(𝑅=𝑃×𝐶) R=P×C?
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0.1 |
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0.2 × 0.5 = 0.1 |
R = P × C |
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| 15 |
Calculate the aggregate risk score if the weights of the criteria are 0.321, 0.388, 0.157, 0.073, and 0.061, and the local risk scores for a route are 0.5, 0.6, 0.4, 0.3, and 0.2 respectively.
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Calculate step-by-step:
• 0.321 × 0.5 = 0.1605
• 0.388 × 0.6 = 0.2328
• 0.157 × 0.4 = 0.0628
• 0.073 × 0.3 = 0.0219
• 0.061 × 0.2 = 0.0122
Add all:
0.1605 + 0.2328 + 0.0628 + 0.0219 + 0.0122 = 0.4902 |
Aggregate risk score = (weight₁ × local risk score₁) + (weight₂ × local risk score₂) + … + (weightₙ × local risk scoreₙ) |
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| 16 |
If the probability assessment for a risk is ranked 3 on a scale of 5 and the severity assessment is also ranked 3, with the transport segment accounting for 20% of the total route distance, calculate the risk assessment using the formula (𝑅=𝑃×𝐶×𝐷) R=P×C×D?
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1.80 |
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R = 3 × 3 × 0.20 = 9 × 0.20 = 1.8 |
Risk assessment R = P × C × D |
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| 17 |
Given that the weight for environmental risk is 0.061 and the local risk score for a route is 0.4, calculate the contribution of environmental risk to the overall risk score.
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0.0244 |
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Contribution = 0.061 × 0.4 = 0.0244 |
Contribution = Weight × Local risk score |
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| 18 |
Calculate the new overall risk score if the weight of infrastructure risk is increased from 0.388 to 0.400 while keeping other parameters constant, given that its local risk score is 0.2.
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0.080 |
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= 0.4 × 0.2 = 0.08 |
Risk contribution = weight × local risk score |
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| 19 |
If a mode of transportation has a risk weight of 0.073 and its risk score is reassessed from 0.4 to 0.35, what is the change in its contribution to the overall risk score?
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0.00365 |
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0.073 × 0.4 = 0.0292
0.073 × 0.35 = 0.02555
= 0.0292 − 0.02555 = 0.00365 |
Contribution = Weight × Risk score
Calculate initial contribution then calculate new contribution
Lastly, calculate the change in contribution
Change = Initial contribution − New contribution |
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| 20 |
If the local weights of freight-damage risk, infrastructure risk, and operational risk are 0.1, 0.2, and 0.15 respectively, what is their total contribution to the risk score if their respective weights are 0.321, 0.388, and 0.157?
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0.14647 |
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= (0.1 × 0.321) + (0.2 × 0.388) + (0.15 × 0.157)
= 0.0321 + 0.0776 + 0.02355
= 0.13325 |
Contribution = sum of (local risk score × weight) |
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