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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 landslide susceptibility mapping is to identify areas that are prone to landslides, so that preventive actions can be taken in advance. This helps reduce economic losses (e.g., property damage) and protect the environment (e.g., prevent soil erosion, loss of vegetation). |
The study “GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh” |
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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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Random Forest |
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The article states that Random Forest had the highest prediction accuracy for landslide susceptibility because it combines many decision trees and reduces errors. |
The study “GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh” |
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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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75%-12%=63% |
percentage subtraction to find the difference between total risk and high-risk zones. |
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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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Total landslide instances = 255
80% used for training
0.80 *255 = 204
Remaining 20% used for testing
255 - 204 = 51 |
basic percentage application commonly used in machine learning to split data into training and testing sets. |
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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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0.09 * 7000 =630km |
Part = Percentage * Total |
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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 = 1 − False Positive Rate (FPR)
= 1 − 0.05
= 0.95 |
Specificity = True Negatives / (True Negatives + False Positives) |
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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 AUC value:
Close to 1.0 indicates a highly accurate model.
AUC of 0.963 shows the model has excellent discriminatory power between classes. |
The AUC measures how well a classification model distinguishes between positive and negative classes.
AUC ≥ 0.90 is generally considered excellent. |
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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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(204/255)(100) =80% |
Percentage = (Part/Whole)(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 = 100% − Error Rate
= 100% − 25%
= 75% |
Accuracy = (Correct Predictions / Total Predictions) * 100 or Accuracy = 1 − 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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Success Rate = (Correct Predictions / Total Predictions) * 100
= (181 / 204) * 100
≈ 88.73% |
Success Rate = (Correct Predictions / Total 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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The article highlights that multimodal transportation systems aim to optimize routes not just for cost and time, but also to reduce environmental impacts and improve safety. |
The study “Multi-objective Optimization of Freight Route Choices in Multimodal Transportation” |
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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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The FAHP-DEA method combines expert judgment with quantitative modeling, helping decision-makers to prioritize multiple transportation risks and optimize route selection based on both efficiency and safety. |
The study “A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems” |
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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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Combined weight of the other three criteria = 1 − (0.157 + 0.073)
= 1 − 0.230
= 0.770 |
In AHP/FAHP models, all criteria weights must add up to 1.
We subtract the known weights from the total to find the rest. |
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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?
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0.1 |
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Risk = 0.2 * 0. 5
=0.1 |
Risk = Probability * Consequence |
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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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Aggregate Risk = (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
= 0.4382 ≈ 0.438 |
Aggregate Risk = (w₁*s₁) + (w₂*s₂) + (w₃*s₃) + (w₄*s₄) + (w₅*s₅) |
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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?
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1.80 |
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R = 3 * 3 * 0.20
= 9 * 0.20
= 1.80
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Risk Score (R) = Probability (P) * Consequence Severity (C) * Distance Ratio (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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0.061 * 0.4 = 0.0244
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Contribution to Overall Risk = Weight * Local Risk Score |
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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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0.080 |
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0.400 * 0.2 = 0.080 |
Contribution to Overall Risk = Weight * Local Risk Score |
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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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Change = 0.073*(0.4-0.35)
=0.073 * 0.05
=0.00365 |
Change in Contribution = Weight * (Old Score - New Score) |
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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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0.14647 |
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Total Contribution = (0.321 * 0.1)+(0.388 * 0.2)+(0.157 * 0.15 )
=0.0321 + 0.0776 + 0.02355
= 0.13325 ไม่มีคำตอบจึงเลือกตัวใกล้เคียง |
Contribution = Weight * Local Risk
Total = (W₁ * R₁) + (W₂ * R₂) + (W₃ * R₃) |
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