| 1 |
What is the primary objective of landslide susceptibility mapping as described in the article?
|
To mitigate the economic and environmental damage by predicting areas at risk. |
|
Mapping is used for landslide prediction and improving safety. |
Mapping uses historical data of both landslide and rainfall to predict where the landslide will occur. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 2 |
Which machine learning algorithm was noted for having the highest success rate according to the article?
|
Random Forest |
|
Random forest is the most accurate for mapping and landslide prediction. |
It can endure the noise and overfitting, has high prediction accuracy. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
|
|
Total landslide-susceptible area = 75% of the district
Highly susceptible zone = 12% of the district |
susceptible but not highly susceptible area = 75%−12%=63%
not highly susceptible area = 100%−12%=88% |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
51 |
|
Total landslides = 255
Training data = 80% of 255 |
Training instances = 255 x 0.80 = 204
Testing instances = 255 - 204 = 51 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
|
|
Area = total area x percentage
Total area = 7,000 km^2
Very high susceptible zone = 9% |
7,000 x (9/100) = 630 km^2 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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.
|
|
|
Specificity = 1 − false positive rate
Logistic regression model = 0.05
True positive rate = 0.95 |
Specificity = 1 − 0.05 = 0.95 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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.
|
Excellent |
|
0.963 is in range of 0.9 and 1.0. |
0.9 – 1.0 = excellent
0.8 – 0.9 = good
0.7 – 0.8 = fair
0.6 – 0.7 = poor
0.5 – 0.6 = fail |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 8 |
If the training dataset consists of 204 locations, calculate the percentage of this training dataset from the total landslide occurrences (255 locations).
|
80% |
|
Percentage=( total occurrences/training dataset size)×100
Training dataset size = 204
Total occurrences = 255 |
Percentage = (204/255) x 100 = 80% |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 9 |
If the model predicts a 25% error rate for new observations, what is the accuracy percentage for predictions made by this model?
|
75% |
|
Accuracy = 100% − error rate
Error rate = 25% |
Accuracy = 100% − 25% = 75% |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 10 |
Calculate the success rate if a model correctly predicted 181 out of 204 training data points.
|
|
|
Success rate = (total predictions/correct predictions ) x 100
Correct predictions = 181
Total predictions = 204 |
Success rate = (181/204) x 100 is around 88. 73% |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 11 |
What is the primary focus of multimodal transportation systems according to the article?
|
Enhancing environmental sustainability and safety. |
|
Primary focus are efficiency, safety, and sustainability. |
Reduce carbon emissions by choosing more eco-friendly modes like rail or waterways.
Decrease traffic jam and pollution on roads.
Improve overall safety by choosing better route planning and compatibility. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 12 |
According to the study, what is the main advantage of using the FAHP-DEA method in risk analysis for multimodal transportation systems?
|
It allows for precise risk prioritization and optimization of routes. |
|
FAHP-DEA allow precise risk prioritization and effective route optimization. |
FAHP helps accurately prioritize risks by handling the uncertainty.
DEA helps optimize the efficiency of different transportation routes. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
|
|
Total weight = 1
Operational risk = 0.157
Security risk = 0.073 |
Remaining weight = 1 − (0.157 + 0.073) = 1 − 0.23 = 0.77 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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 |
|
Probability of accident = 0.2
Consequence severity = 0.5 |
R = 0.2×0.5 = 0.1
Risk level = 0.1 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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.
|
|
|
Aggregate Risk Score= Sigma (Weight x Local Risk Score) |
Aggregate risk score = (0.321 x 0.5) + (0.388 x 0.6) + (0.157 x 0.4) + (0.073 x 0.3) + (0.061 x 0.2) = 0.1605 + 0.2328 + 0.0628 + 0.0219 + 0.0122 = 0.1605 + 0.2328 + 0.0628 + 0.0219 + 0.0122 = 0.4902 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
1.80 |
|
P (Probability rank) = 3
C (Severity rank) = 3
D (Proportion of distance) = 20% = 0.20 |
R = 3 x 3 x 0.20 = 9x 0.20 = 1.8
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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.
|
0.0244 |
|
Contribution = weight x local risk score
Weight for environmental risk = 0.061
Local risk score = 0.4 |
Contribution = 0.061 x 0.4 = 0.0244 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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.
|
0.080 |
|
Weight of infrastructure risk from 0.388 to 0.400
Local risk score = 0.2 |
Original Contribution = 0.388 x 0.2 = 0.0776
New Contribution = 0.400 x 0.2 = 0.080
Change = 0.080 − 0.0776 = 0.0024 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
0.00365 |
|
Change = weight x (old score − new score)
Weight = 0.073
Old risk score = 0.4
New risk score = 0.35 |
Change = 0.073 x (0.4 − 0.35) = 0.073 x 0.05 = 0.00365 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 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?
|
0.14647 |
|
Total contribution to the risk score= sum of each local risk score x weight |
Freight-damage contribution = 0.1 x0.321 = 0.0321
Infrastructure contribution = 0.2 x 0.388 = 0.0776
Operational contribution = 0.15 x 0.157 = 0.02355
Total contribution = 0.0321 + 0.0776 + 0.02355 = 0.13325 |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|