| 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. |
|
Landslide susceptibility mapping identifies areas prone to landslides, aiding land-use planning. Data quality significantly influences landslide susceptibility modeling accuracy and generalizability. |
The objective of landslide information is to identify which relatively landslide-susceptible areas are best suited for what types of development activities. |
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?
|
All of the above equally |
|
Logistic Regression: Used for binary classification tasks (e.g., predicting yes/no outcomes). It estimates probabilities using a logistic function.
Decision Trees: These models predict the value of a target variable by learning simple decision rules inferred from the data features.
Random Forests: An ensemble of decision trees, typically used for classification and regression, improving model accuracy and overfitting control. |
According to https://www.simplilearn.com/ |
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?
|
63% |
|
These indicators can be calculated as follows: FR = S i / S A i / A , FD = N i / A i where Si is the landslide area in each susceptibility zone, S is the total area of landslides, Ai is the area of a specific landslide susceptibility zone, A is the total area of the study area, and Ni is the number of landslides. |
The area is 75% susceptible to landslides.
The. highly susceptible zone covers approximately 12% of the district.
So , the area that is not highly susceptible is 75% -12% = 63% |
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 |
|
These indicators can be calculated as follows: FR = S i / S A i / A , FD = N i / A i where Si is the landslide area in each susceptibility zone, S is the total area of landslides, Ai is the area of a specific landslide susceptibility zone, A is the total area of the study area, and Ni is the number of landslides. |
The landslide instances were used for testing calculated by 255-(255*80%) = 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?
|
630 km² |
|
Can be calculated by taking the total area. multiply by the percentage of the very high susceptible zone. |
The area of the very high susceptible zone can calculated by 7000*9% = 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.
|
1.00 |
|
Precision however, is not affected by a large number of negative samples, that’s because it measures the number of true positives out of the samples predicted as positives (TPR+FPR). |
Can be calculated by Precision = TP/(TPR+FPR) = 0.95/(0.5+0.95) = 1 |
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 |
|
In general, AUC values are interpreted as follows: 0.5-0.6 (failed), 0.6-0.7 (worthless), 0.7-0.8 (poor), 0.8-0.9 (good), > 0.9 (excellent). |
the model's prediction will be excellent because 0.963 > 0.9 . |
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% |
|
To determine the percentage, we have to divide the value by the total value and then multiply the resultant by 100. |
Can be calculated by (204*100)/255 = 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 = (Number of Correct Predictions) / (Total Number of Predictions). |
Can be calculated by ((100-25)/100)*100 = 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.
|
88.73% |
|
To determine the percentage, we have to divide the value by the total value and then multiply the resultant by 100. |
Can be calculated by (181*100)/204 = 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?
|
Ignoring the risks associated with transportation. |
|
Multimodal transportation is a strategy that integrates different modes of transport to optimize the movement of goods or people. |
It offers numerous benefits, including enhanced connectivity, reduced congestion, lower costs, improved logistics, and increased sustainability. |
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. |
|
The proposed FAHP-DEA methodology uses the FAHP method to determine the weights of each risk criterion. The DEA method is employed to evaluate the linguistic variables and generate the risk scores. The simple additive weighting (SAW) method is used to aggregate risk scores under different risk criteria into an overall risk score. |
According to a case study of the coal industry demonstrates that the proposed risk analysis model is practical and allows users to more accurately prioritize risks while selecting an optimal multimodal transportation route. The process raises user’s attention to the high-priority risks and is useful for industries in optimizing a multimodal transportation route under risk decision criteria. |
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?
|
0.770 |
|
Can be calculated by 1-(0.157+0.073) = 0.770 |
|
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?
|
0.7 |
|
Can be calculated by 0.5+0.2 = 0.7 |
|
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.
|
0.438 |
|
Can be calculated by ((0.321/0.5)+(0.388/0.6)+(0.157/0.4)+(0.073/0.3)+(0.061/0.2))/5 = 0.438 |
|
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 |
|
Can be calculated by (20*3*3)/100 = 1.80 |
|
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 |
|
Can be calculated by 0.061*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 |
|
The risk score is the result of your analysis, calculated by multiplying the Risk Impact Rating by Risk Probability. |
Can be calculated by 0.400*0.2 = 0.080 |
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?
|
0.00585 |
|
The risk weight used to convert holdings into risk-weighted equivalent assets would be calculated by multiplying the derived capital charge by 12.5 |
Can be calculated by 0.073/12.5 = 0.00585 |
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?
|
0.10058 |
|
Banks calculate risk-weighted assets by multiplying the exposure amount by the relevant risk weight for the type of loan or asset. |
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|