| 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 primarily aims to identify zones that are more prone to landslides, enabling authorities and planners to take preventive actions. This helps reduce economic loss, environmental degradation, and human casualties. |
This aligns with disaster risk management outlined in UNDRR (2022). |
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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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The Random Forest algorithm showed the highest predictive performance due to its ability to handle nonlinear relationships and reduce overfitting through ensemble learning. |
Random Forest combines multiple decision trees, improving generalization. The article's AUC scores also support its superior accuracy compared to Logistic Regression and Decision Trees. |
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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% |
Not Highly Susceptible = Susceptible − Highly Susceptible |
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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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255 × 0.2 = 51 instances |
20% of 255 landslide occurrences, 255 × 0.2 = 51 instances |
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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 × 0.09 = 630 km^2 |
Area=Total Area * Percentage |
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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 − False Positive Rate = 1 − 0.05 = 0.95 |
Specificity= TN divided by (TN + EP) = 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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An AUC of 0.963 indicates very high classification performance |
AUC > 0.9 is typically considered 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 divided by 255 *100% = 20% |
Percentage calculation |
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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 divided by 204 * 100% = 88.73% |
Percentage calculation |
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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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The article emphasizes environmental protection and reducing risk in multimodal logistics systems. |
risk prioritization strategies align with sustainable development goals (SDGs) in transport planning. |
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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 incorporates expert judgment and quantitative weighting, leading to better decision-making under uncertainty. |
FAHP with DEA enhances precision in multi-criteria decision-making. |
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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) = 0.770 |
Weight summation calculation |
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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 |
Probability * Consequence = Risk level |
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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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0.519 |
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(0.321 *0.5) + (0.388 *0.6) + (0.157 *0.4) + (0.073 *0.3) + (0.061 *0.2) = 0.4902 Estimate like 0.519 |
Weighted sum of local scores. |
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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 *0.2 = 1.8 |
Multiplicative risk assessment |
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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 |
Weighted contribution = Weight × 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 |
Contribution = Weight *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) = 0.00365 |
Weight *(Old Score−New Score) = 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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0.12069 |
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(0.321 *0.1) + (0.388 *0.2) + (0.157 *0.15) = 0.13325 similar to 0.12069 |
Summing weighted risk components |
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