| 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 risk mapping has a primary goal to reduce disaster damage through the analysis of risk areas using GIS technology and statistical/artificial intelligence models, which will be useful for both researchers, planners and policymakers at local and national levels. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh by Md. Sharafat Chowdhury et el. |
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?
|
Decision and Regression Tree |
|
from research “The success rate shows the area under the ROC for LR, RF and DRT models are 0.943, 0.917 and 0.947, respectively.”. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh by Md. Sharafat Chowdhury et el. |
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% |
|
The area "at risk of landslides" but "not very high" is 63% of Chattogram district. This is an area that should receive attention for preventive management, but may not be as urgent as the very high risk area. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh by Md. Sharafat Chowdhury et el., from the article “The LSMs showed that almost 9–12 % of areas of the Chattogram district are highly susceptible to landslides.” and the equation 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 |
|
The number of landslide events used for testing the model in this paper is 51, which is 20% of the total (255), according to the data partitioning principle in machine learning research. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh by Md. Sharafat Chowdhury et el., from the article “The inventory data was divided into training and testing ... 80% (204 landslide locations) were used for training purposes and 20% (51 landslide locations) were used for testing purposes.”. |
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 |
|
Since the area in the “Very High Susceptible Zone” is 9%, the answer after calculation is 630 km^3. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh by Md. Sharafat Chowdhury et el., from the article “...almost 9–12% of areas of the Chattogram district are highly susceptible to landslides.”. |
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 |
|
From the calculation results, it can be seen that 0.95 is the specificity of the model. |
Calculated from 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 |
|
This indicates that the logistic regression model has very high discriminatory power, discriminating landslide-prone and non-landslide-prone areas with almost perfect accuracy, as the score threshold is 0.9-1, which is Excellent. |
"The ROC values for training data were 0.943, 0.917, and 0.947, and testing data were 0.963, 0.934, and 0.905 for LR, RF, and DRT models, respectively. The accuracy is higher than the previous research [...] The performance is above 0.7 indicating an excellent performance of the model." Based on research by Chowdhury et al., 2023. |
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 research stated in the abstract that the data used for training will use 80% of all data. |
Based on research by Chowdhury et al., 2023. From abstract that said " The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets.". |
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% |
|
The error rate of this model is consistent with Accuracy as 100%-25% = 75%, so the answer is 75%. |
Based on research by Chowdhury et al., 2023. From 3.2.2. Random forest model in page 10 said "It is understood from Fig. 4 that the resulting model will produce a 25 % error rate for new observations. So, for a reasonably good model, 75 % of the results will be accurate.". |
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 answer is 88.725%, which can be mathematically estimated to be 88.73%. |
Use the equation Success Proportion = (Predicted Area/Total Area)*100. |
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. |
|
From reading and analyzing the research, it can be concluded that the primary goal of this research is Enhancing environmental sustainability and safety. |
Reference from A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems by Kwanjira Kaewfak et al., from abstract that said "Multimodal transportation has become a main focus of logistics systems due to environmental concerns, road safety issues, and traffic congestion. Consequently, research and policy interests
in multimodal freight transportation problems are increasing.". |
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. |
|
Using FAHP-DEA cannot eliminate all risks, but it can help predict risks and find appropriate solutions. |
Reference from A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems by Kwanjira Kaewfak et al. |
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 |
|
From the calculation, the result is 0.770. |
Using equation, 1 = sum of criteria weight. |
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 |
|
Calculated using 0.2 * 0.5 = 0.10 |
Using equation, Risk level = Probability of accident * Consequence severity |
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 |
|
The result of the calculation is that the aggregate risk score = 0.4902 or approximately 0.49. In the options, there is no answer, so the answer that is closest is chosen. |
อ้างอิงจาก สมการ aggregate risk score = ∑(Weight of the criteria ×Local risk Score) |
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= 3×3×0.2= 1.8
|
Based on the equation, 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 |
Based on the equation, contribution=Weight×Local Risk Score. |
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 |
|
new contribution= 0.400×0.2= 0.080
|
Based on the equation, contribution=Weight×Local Risk Score and new contribution=New Weight×Local Risk Score |
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 |
|
Step 1: Calculate old contribution
0.073*0.4=0.0292
Step 2: Calculate new contribution
0.073*0.35=0.02555
Step 3: Find the change
0.0292−0.02555= 0.00365 |
Based on the equation, contribution=Weight×Local Risk Score , new contribution=New Weight×Local Risk Score and sum = new contribution - contribution. |
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 |
|
The result of the calculation is that the aggregate risk score = 0.13325 or approximately 0.133. In the options, there is no answer, so the answer that is closest is chosen. |
Based on the equation, contribution=Weight×Local Risk Score and sum = contribution + contribution+contribution. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|