| 1 |
Which factor is considered a major driver of land cover change contributing to landslides in the Chattogram District?
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Hill cutting and unplanned urbanization |
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These activities disrupt the natural stability of the land and can significantly increase the risk of landslides. |
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| 2 |
What does the ROC value for a model indicate in the context of this study?
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The accuracy of the model in predicting landslide susceptibility |
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The ROC curve is used to assess the performance of a classification model by plotting the true positive rate against the false positive rate at various threshold settings. |
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| 3 |
According to the study, what percentage of the Chattogram District's area is highly susceptible to landslides?
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15-20% |
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15-20% of the Chattogram District's area is highly susceptible to landslides. |
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7 |
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| 4 |
How are the logistic regression model's coefficients used in landslide susceptibility mapping?
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To reflect the contributions of each factor affecting landslides |
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These coefficients indicate how each factor (e.g., slope, soil type, land use) influences the probability of a landslide occurring, allowing for the assessment of their relative importance in the model. |
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| 5 |
What is the importance of the Stream Density factor according to the Random Forest model in the document?
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One of the top five most important factors |
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Stream density is crucial because areas with higher stream density can be more prone to erosion and instability, contributing significantly to landslide risk. |
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| 6 |
According to the document, which machine learning model showed the highest success rate in training data?
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Random Forest |
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Random Forest is often noted for its robust performance in classification tasks, including landslide susceptibility mapping. |
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| 7 |
What is the primary geological characteristic of the Chattogram District that contributes to landslide susceptibility?
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Folded anticlines and synclines with unconsolidated sedimentary rocks |
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These geological features can create unstable slopes and contribute to landslide risk, especially when combined with other factors like heavy rainfall and land use changes. |
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7 |
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| 8 |
How do land use and land cover (LULC) changes influence landslide occurrences in the Chattogram District?
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They increase landslide risk due to deforestation and construction |
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Changes such as deforestation and unplanned urban development can destabilize slopes, reduce vegetation cover, and alter natural drainage patterns, all of which contribute to a higher likelihood of landslides. |
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| 9 |
What percentage of total variance is explained by the first factor in the factor analysis discussed in the document?
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51.29% |
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This indicates that the first factor has a significant impact on the variability in the data related to landslide susceptibility. |
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| 10 |
According to the factor analysis, which factor is related to the cost and sufficiency of manure?
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Factor 3: Correlation between manure sufficiency and expenses (cost) |
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This factor specifically addresses the relationship between the adequacy of manure and the associated costs. |
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| 11 |
According to the factor analysis, which factor is related to the cost and sufficiency of manure?
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Factor 3: Correlation between manure sufficiency and expenses (cost) |
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This factor specifically addresses the relationship between the adequacy of manure and the associated costs. |
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7 |
-.50
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| 12 |
What is the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy reported in the document?
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0.800 |
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This value indicates a good level of sampling adequacy for factor analysis. |
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| 13 |
Which of the following statements best describes the contribution of Factor 2 in the factor analysis?
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It is related to soil analysis and plant nutrient utilization. |
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Factor 2 in the factor analysis focuses on aspects of soil analysis and the utilization of plant nutrients. |
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| 14 |
Which factor is primarily associated with limitations in the utilization of chemical fertilizer and manure according to the document?
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Factor 4 |
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This factor addresses issues related to the constraints and challenges faced in the application of these agricultural inputs. |
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| 15 |
What is the percentage of variance explained by all four factors together?
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60% |
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This indicates that the combined factors account for 60% of the total variance in the dataset, providing a comprehensive understanding of the variables analyzed. |
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| 16 |
What is the highest mean value for the propositions used in the factor analysis, according to the document?
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3.000 |
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the highest mean value for the propositions used in the factor analysis is 3.000. |
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| 17 |
What was the minimum magnitude for the factor loads considered for interpreting the analysis results in the factor analysis?
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0.50 |
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This threshold is commonly used to ensure that the factor loadings are strong enough to be considered significant in the interpretation of the factors. |
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| 18 |
According to the document, how many factors were initially considered before deciding on the final number?
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5 |
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5 factors were initially considered before deciding on the final number of factors for the analysis. |
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| 19 |
Which method was used for rotation in the factor analysis described in the document?
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Varimax |
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Varimax is a commonly used method for orthogonal rotation that simplifies the interpretation of factor loadings by maximizing the variance of the squared loadings of each factor. |
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| 20 |
Based on the factor analysis, how is Factor 1 defined in the document?
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Chemical fertilizer and manure utilization level and efficiency perception |
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This factor typically encompasses the perceptions and efficiency related to the utilization of chemical fertilizers and manure. |
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