| 1 |
What is the purpose of the empirical case study on coal manufacturing in the paper?
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To demonstrate the proposed decision support model |
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The empirical case study of coal manufacturing is conducted to demonstrate the proposed model. This methodology can provide a guidance for effectively determining the multimodal transportation routes to improve performance of logistics systems. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation |
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| 2 |
Which factor does the model NOT consider in route selection for a multimodal transportation network?
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Cultural preferences |
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Therefore, considering the impact factors such as the transport cost, time, and comprehensive risk assessment model were further created. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation |
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| 3 |
What is the role of expert judgments in the decision support model?
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They influence the weights obtained from AHP |
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AHP is employed to determine weights of each factor, which rely on expert judgments. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation |
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| 4 |
What logistics system aspect does the proposed methodology aim to improve?
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Employee satisfaction |
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However, considering all these factors makes route selection a very complex multi-objective decision-making problem. To address this important issue, many scholars have been developing mathematical programing models to optimize route selection to improve the logistics performance. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation |
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| 5 |
Essay | Describe the role of Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP) in the decision support model for determining an optimal multimodal transportation route. Explain how these methodologies contribute to the model's effectiveness and discuss any potential limitations.
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It utilized the AHP and zero-one goal programing (ZOGP) to solve the optimal route for the multimodal transportation problem. The presented AHP-ZOGP model has competitive performance compared with other techniques for determining
multimodal transportation routes and the potential limitation is risk. |
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This comprehensive classification not only helps researchers and practitioners identify and classify the potential transportation factors, but also provides a starting point for creating a risk transportation index model applicable to the multimodal transportation process. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation |
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| 6 |
What is the role of the FAHP method in the proposed risk analysis model?
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To determine the weights of each risk criterion |
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The proposed FAHP-DEA methodology uses the FAHP method to determine the weights of each risk criterion. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems |
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| 7 |
Which industry is used as a case study in the proposed risk analysis model?
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Coal |
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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. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems |
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| 8 |
What does the DEA method do in the proposed FAHP-DEA methodology?
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Evaluates linguistic variables and generates risk scores |
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The DEA method is employed to evaluate the linguistic variables and generate the risk scores. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems |
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| 9 |
Which method is used to aggregate risk scores into an overall risk score in the proposed model?
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Simple Additive Weighting (SAW) |
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The simple additive weighting (SAW) method is used to aggregate risk scores under different risk criteria into an overall risk score. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems |
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| 10 |
Essay | Using the coal industry case study, please explain how the proposed risk analysis model is practical and aids in prioritizing risks. Discuss how this model can be beneficial for industries in optimizing multimodal transportation routes under risk decision criteria.
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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. |
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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. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems |
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| 11 |
How were geotechnical parameters of soils at landslide-prone sites evaluated in the study?
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Laboratory experiments |
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Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas |
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| 12 |
What modeling techniques were used to assess the probability of landslide occurrence in the future?
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Autoregressive Moving Average (ARIMA) model |
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Through extensive field surveys, we assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas |
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| 13 |
What is the potential application of the study's findings in hazard management?
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Devising countermeasures for managing landslides |
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This work shall help devise countermeasures for managing the landslides in the study area locally for hazard management in the Himalayas. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas |
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| 14 |
How does the study aim to contribute to hazard management in the Himalayas?
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By serving as a guiding framework for using artificial intelligence and machine learning |
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This work shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas |
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| 15 |
Essay | Explain the methodology employed in the study to evaluate geotechnical parameters and assess the probability of future landslide events. Discuss the potential implications of using artificial intelligence and machine learning in hazard management in the Himalayas, with reference to the study's guiding framework.
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Through extensive field surveys, we evaluated different geotechnical parameters of soils at the most landslide-prone site along the Highway and augmented it with the satellite remote sensing datasets to determine the threshold values that trigger a landslide and assess the probability of occurrence of landslide events in the future using Autoregressive Moving Average (ARIMA) model and IBM SPSS Forecasting Model. This work shall help devise countermeasures for managing the landslides in the study area locally and shall serve as the guiding framework for using artificial intelligence and machine learning techniques for hazard management in the Himalayas. |
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DEM (Digital Elevation Model) (Carto DEM 10-m) was used in this
study and is the digital representation of the topographic surface of the earth. It is widely used to evaluate landslide-prone zones (slope gradient, landslide assessment, and geomorphologic analysis). Thus, creating an accurate terrain surface model is the key to developing computer-based modeling tools for landslide hazard assessment in mountainous areas. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas |
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| 16 |
How was the landslide inventory database divided for training and testing in the research?
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80% training, 20% testing |
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The landslide inventory database (255 locations) was randomly divided into training (80 %) and testing (20 %) sets. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
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| 17 |
Which machine learning model showed the highest prediction rate among LR, RF, and DRT?
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Logistic Regression (LR) |
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Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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| 18 |
What do the ROC values for training and testing data signify in the context of landslide susceptibility mapping?
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The accuracy of the machine learning models |
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The accuracy is higher than the previous research in comparison to the extent of the study area and the size of the inventory. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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| 19 |
Which model is considered more realistic according to susceptibility zones in the research?
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Decision and Regression Tree (DRT) |
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According to susceptibility zones, DRT is the more realistic model followed by LR. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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| 20 |
Essay | Compare and contrast the performance of Logistic Regression (LR), Random Forest (RF), and Decision and Regression Tree (DRT) models in landslide susceptibility mapping. Discuss the strengths and limitations of each model based on the research findings.
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The confusion matrix of the LR model indicates that 171 landslide absences and 189 landslide presences were correctly predicted.
In the random forest model, data were split into training (80 %) and testing (20 %).
The R programming language and the part package were used to construct the regression trees in the current study. |
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The success rate shows the area under the ROC for LR, RF and DRT models are 0.943, 0.917 and 0.947, respectively. The prediction rate shows the area under the ROC for LR, RF and DRT models are 0.963, 0.934 and 0.890, respectively. The result is above 0.7 indicating an excellent performance of the model. Though machine learning models produce better results than other models, in this research, the LR model showed a better prediction rate compared to other models machine learning models. DRT model misclassified some landslides and the RF model classified comparatively larger extent of very high and high susceptibility classes which ultimately affected AUC values leading to higher accuracy in the LR model. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh |
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