| 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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Landslide susceptibility mapping is a proactive tool to forecast vulnerable zones and minimize damage, not just an academic exercise or geological mapping without practical use. |
Springeropen |
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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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Decision and Regression Tree |
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Decision and Regression Tree (DRT) is recognized as having the highest success rate in the comparative analysis of machine learning models for the study. |
Pubmed |
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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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63% |
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75%−12%=63% |
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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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204 |
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255(80/100) =204 |
percent |
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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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7000(9/100) =630 |
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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−FPR=1−0.05=0.95 |
Specificity |
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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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AUC = 0.5 → no better than random guessing
AUC 0.6–0.7 → poor discrimination
AUC 0.7–0.8 → fair
AUC 0.8–0.9 → good
AUC 0.9–1.0 → excellent to outstanding performance
ans = 0.963 |
Interpretation of AUC Value |
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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% =0.8 |
percent |
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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% |
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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 =88.725 |
percent |
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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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lements show that the model’s goal goes beyond just minimizing time or cost—it actively seeks routes that are environmentally sustainable and safe, aligning with broader global and regulatory trends in green logistics |
Sciencedirect |
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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 (Fuzzy Analytic Hierarchy Process) is used to capture expert input and establish the relative importance (weights) of various risk criteria—this handles uncertainties and subjectivity effectively.
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researchgate |
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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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combined weight of the remaining three criteria |
sum minus |
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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 |
multiply |
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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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0.438 |
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sigma |
Aggregate Risk Score |
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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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3×3×0.20=9×0.20=1.8 |
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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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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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0.100 |
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0.388 to 0.400 while keeping other parameters constant, given that its local risk score is 0.2. |
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×(0.4−0.35)=0.073×0.05=0.00365 |
Weight×(Old Risk Score−New Risk Score) |
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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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0.15788 |
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(0.321×0.1)+(0.388×0.2)+(0.157×0.15) |
∑(Weight×Local Risk Score) |
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