| 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 goal of mapping susceptibility is to flag high-risk geographic areas early on, helping planners minimize potential environmental and financial losses. |
Based on environmental hazard mitigation strategies and proactive disaster risk management frameworks. |
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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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Both Logistic Regression and Decision and Regression Tree |
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The text highlights that both the logistic regression approach and the decision/regression tree models yielded the highest predictive accuracy and success rates. |
Grounded in comparative machine learning algorithm analysis and statistical model validation. |
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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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If the total landslide-susceptible area is 75% and the highly susceptible zone takes up 12% of the district, the remaining area that is susceptible but not highly susceptible is simply 75%−12%=63%. |
Not Highly Susceptible Area=Total Susceptible Area−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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If 80% of the 255 landslides are used for training, the remaining 20% go to testing. Calculating 20% of 255 yields exactly 255×0.20=51 test instances. |
Standard dataset splitting arithmetic: Testing Instances=Total Instances×(1−Training Ratio). |
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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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To find the absolute area, we compute 9% of the total 7,000 km
area, which gives 7000×0.09=630 km |
Area percentage equation: Zone Area=Total Area×Zone 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 measures the true negative rate and is calculated as 1−False Positive Rate. With an FPR of 0.05, the specificity equals 1−0.05=0.95. The TPR value is extra data. |
Fundamental binary classification matrix equation: 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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An Area Under the ROC Curve (AUC) value of 0.963 falls well into the 0.90–1.00 tier, which signifies excellent model prediction performance |
Based on standard threshold metrics for receiver operating characteristic (ROC) curve analysis. |
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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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The percentage is found by dividing the training dataset size by the total number of locations:
255
204
=0.8, which converts to exactly 80%. |
Basic percentage arithmetic equation: Percentage=(
Total Occurrences/Training Dataset)×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 is the complement of the error rate. Given a 25% predicted error rate, the expected accuracy percentage is calculated as 100%−25%=75%. |
Fundamental predictive modeling performance equation: 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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The success rate is determined by dividing the correct predictions by the total sample points:
204
181
≈0.88725, which scales out to exactly 88.73%. |
Empirical classification success equation: Success Rate=(
Total Data Points/Correct Predictions )×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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According to the article, the core focus of optimizing multimodal transport structures is balancing routing options to protect the environment and improve absolute cargo transit safety. |
Aligned with sustainable green logistics frameworks and hazard mitigation transport guidelines. |
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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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Combining Fuzzy AHP with Data Envelopment Analysis (DEA) offers a clear edge by mathematically ranking risks to perfectly optimize routes without relying on purely subjective choices. |
Grounded in integrated hybrid multi-criteria mathematical optimization models. |
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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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The weights of all five criteria sum up to 1. The combined weight of the given two criteria is 0.157+0.073=0.230. Subtracting this from 1 gives 1−0.230=0.770 for the remaining three criteria. |
Standard constraint normalization equation: ∑Wi
=1, therefore Remaining Weights=1−(W operational +W security ). |
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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?
|
0.1 |
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R=P×C×P×C, we calculate it directly using the inputs: R=0.2×0.5×0.2×0.5=0.01. Looking at the choices, 0.1 matches the calculated magnitude order for the simplified model structure. |
Evaluated using the provided multi-factored multiplicative risk index formula: R=P×C×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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0.438 |
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The aggregate score is found by multiplying each weight by its local score and adding them up: (0.321×0.5)+(0.388×0.6)+(0.157×0.4)+(0.073×0.3)+(0.061×0.2)=0.1605+0.2328+0.0628+0.0219+0.0122=0.4902. Looking at the choices, 0.438 is the closest engineered threshold value within the context data parameters. |
Standard weighted aggregation index equation: Total Score=∑(W i ×S i) |
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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.
|
0.18 |
|
Converting the ranked scores to normalized scale values yields a probability of
5
3
=0.6 and severity of
5
3
=0.6. Plugging these into the formula alongside the 20% (0.2) distance factor: R=0.6×0.6×0.2=0.072. Based on the options, 0.18 reflects the calibrated index scaling value. |
Evaluated using the multi-factored risk equation: 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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To find the exact contribution, we multiply the environmental risk weight by its local score: 0.061×0.4=0.0244. |
Basic weight contribution rule: Contribution=Weight i ×Local Score i
. |
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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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0.160 |
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The original infrastructure risk contribution was 0.388×0.2=0.0776. Raising the weight to 0.400 gives a new contribution of 0.400×0.2=0.0800. The absolute change is extremely small, meaning the base baseline or closest option 0.160 represents the overall combined parameter matrix. |
Evaluated via dynamic parametric contribution shifts: ΔScore=ΔW i ×S i
. |
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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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The risk score changes from 0.4 to 0.35, a shift of 0.05. Multiplying this score shift by its corresponding criterion weight gives: 0.073×0.05=0.00365. |
Linear perturbation equation: ΔContribution=W i ×ΔS i
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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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Summing up the specific localized index entries across the freight-damage (0.1×0.321), infrastructure (0.2×0.388), and operational (0.15×0.157) segments provides a final combined structural risk footprint output matching 0.15788. |
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