| 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. |
|
Identify and predict areas are at high risk of landslides. |
Heliyon - GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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?
|
Both Logistic Regression and Decision and Regression Tree |
|
They using statistical, machine and deep learning techniques. |
Heliyon - GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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% |
|
Not highly susceptible is 75-12=63% |
Not highly susceptible = the area of susceptible to landslides - highly susceptible zone |
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 |
|
255*80%=204
testing instances is 255-204=51 |
Total landslides -(Total landslides * 80% used for training ) = landslide instances were used for testing. |
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 |
|
7,000 km^2 * 9% =630 km^2
|
The total area(km^2) * 9% of the very high susceptible zone covers = the very high susceptible zone (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.
|
0.95 |
|
Specificity = 1-0.05 =0.95 |
Specificity = 1 - FPR |
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 |
|
The model’s prediction accuracy is rated as excellent , based on its AUC of 0.963. |
Heliyon - GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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% |
|
The percentage of training dataset = 204*100 / 255=80 % |
The percentage of training dataset = the training dataset * 100 / the total landslide occurrences |
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 = 100-25=75 % |
Accuracy = 100% - Error Rate |
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% |
|
The success rate = 181*100/204 =88.725 % |
The success rate = A model correctly predicted * 100 / training data points |
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. |
|
Multimodal transportation has become a main focus of logistics systems due to environmental concerns, road safety issues, and traffic congestion. |
Digital Object Identifier : A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight
Transportation Systems |
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. |
|
Used for identifying and assessing quantitative risks. |
Digital Object Identifier : A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight
Transportation Systems |
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 |
|
0.157+0.073=0.23
1-0.23 =0.77 |
the total sums - ( sum of the operational risk and security risk) = the remaining three criteria |
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 |
|
the risk level = 0.2*0.5=0.1 |
R=P×C |
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.
|
0.519 |
|
Aggregate Risk Score = (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 |
R=P×C |
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 |
|
R = 3*3*0.20 =1.8 |
R=P×C×D |
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 = 0.061*0.4=0.0244 |
R=P*C |
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.
|
0.120 |
|
|
R=P*C |
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 = 0.073 * (0.4-0.35)= 0.00365 |
Change = Weight * (Old score-New score) |
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?
|
0.12069 |
|
0.1 *0.321 = 0.0321
0.2 * 0.388 = 0.0776
0.15 * 0.157 = 0.02355
0.0321 + 0.0776 + 0.02355 =0.133 |
R=P*C |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|