| 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. |
International Journal of Computational Intelligence Systems
https://www.atlantis-press.com/journals/ijcis/125952845/view |
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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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The other is in appropriate freight multimodal transportation route selection is a strategy to improve the performance of logistics and transportation. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation
https://www.atlantis-press.com/journals/ijcis/125952845/view |
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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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Criteria Weighting in AHP:
Expert judgments are often solicited to assign weights to different criteria in the Analytic Hierarchy Process (AHP). AHP involves pairwise comparisons of criteria to determine their relative importance. Experts provide judgments on the significance of each criterion, and these judgments are used to establish the weights that guide the decision-making process. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation
https://www.atlantis-press.com/journals/ijcis/125952845/view |
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| 4 |
What logistics system aspect does the proposed methodology aim to improve?
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Marketing strategies |
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Route selection strategy has become the main aspect in the multimodal transportation system. |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation
https://www.atlantis-press.com/journals/ijcis/125952845/view |
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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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This paper develops a decision support model using an analytic hierarchy process (AHP) and zero-one goal programing (ZOGP) |
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ZOGP is a well-known modification and extension of linear programming. It became a widely applied technique due to its ability to handle decisions of multiple conflicting goals. However, ZOGP has an inability to weight coefficients. Many applications find its usage necessary in combination with other methods, such as AHP, to properly weight coefficients.
The ZOGP model has been applied very frequently because it is simple to use and understand [33]. This technique is used to minimize the deviation from several objectives because of limited resources. To achieve this, the problem is generally formulated by using the ZOGP model. ZOGP can be used to select the alternatives because of the binary nature of the selection variables and the multiple conflicting criteria involved. In this research, we looked forward to finding the optimal multimodal transportation routes using a multi-objective optimization approach. Owing to the complexity of the transportation data, ZOGP was utilized to solve large-scale problems.
Therefore, ZOGP is now introduced. The purpose of this conceptual model is to avoid the complexity of MCDM problems which includes many decision criteria. This model organizes the decision criteria into clusters with hierarchical structure. The objective function selects the most appropriate alternative from the total deviation of main decision criteria in the highest layer of the model, while the deviation of main decision criteria was computed by constrained functions which are identified deviation between deviation of sub-decision criteria and their maximum deviation.
The proposed route selection model integrates AHP and a multi-objective o |
Multi-objective Optimization of Freight Route Choices in Multimodal Transportation
https://www.atlantis-press.com/journals/ijcis/125952845/view |
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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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In the context of the paper "A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation System," the Fuzzy Analytic Hierarchy Process (FAHP) method plays a crucial role in the risk analysis model. The FAHP method is a decision-making technique that incorporates fuzzy logic and the Analytic Hierarchy Process (AHP) to handle imprecise and uncertain information in the risk assessment process |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems
https://ieeexplore.ieee.org/document/9173663 |
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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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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
https://www.nstda.or.th/openarchive/nstda-research-publications/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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Determines weights |
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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
https://ieeexplore.ieee.org/document/9173663 |
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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
https://ieeexplore.ieee.org/document/9173663 |
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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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Contextualizing the Model with the Coal Industry:
In the coal industry, where timely and efficient transportation is paramount, the proposed risk analysis model offers a tailored approach. The model considers the intricacies of multimodal transportation, acknowledging the diverse risk factors associated with coal logistics, including supply chain disruptions, infrastructure vulnerabilities, and geopolitical uncertainties. |
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Practicality of the FAHP-DEA Model:
Criteria Customization for the Coal Industry:
The FAHP phase allows for the customization of risk criteria, tailoring the model to industry-specific challenges. Criteria such as transportation cost, delivery time, and geopolitical stability are given due consideration based on expert judgments from the coal industry.
Handling Uncertainty in Risk Assessment:
Fuzzy logic in FAHP accommodates uncertainties inherent in risk assessments. The coal industry, characterized by fluctuating market demands and regulatory uncertainties, benefits from a model that can incorporate imprecise information, providing a more realistic risk profile.
Expert Input Integration:
The involvement of industry experts in the FAHP phase ensures that the model incorporates domain-specific knowledge. These experts contribute to the weighting of risk criteria, reflecting the unique perspectives and priorities of the coal industry stakeholders. |
A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems
https://www.nstda.or.th/openarchive/nstda-research-publications/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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Interviews with local residents |
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The present study aims to characterize various factors and their threshold values responsible for triggering a landslide. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas
https://www.sciencedirect.com/science/article/pii/S2667010022001809 |
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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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The highly active prone landslide area was identified using remote sensing and aerial investigation techniques, |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas
https://www.sciencedirect.com/science/article/pii/S2667010022001809 |
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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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The potential applications of the study's findings in hazard management, particularly regarding the assessment of landslide occurrence in the western Himalayas using ARIMA (AutoRegressive Integrated Moving Average) and SPSS (Statistical Package for the Social Sciences) statistics, could be significant. Here are some potential applications: |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas
https://ui.adsabs.harvard.edu/abs/2022EnvCh...900624F/abstract |
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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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The study, based on RIMA (Seasonal AutoRegressive Integrated Moving Average) and SPSS (Statistical Package for the Social Sciences) statistics for assessing landslide occurrence in the western Himalayas, likely aims to contribute to hazard management in several ways:
Temporal Pattern Identification:
By using RIMA, a seasonal extension of ARIMA, the study aims to identify temporal patterns in landslide occurrences. Understanding the seasonal variations and trends is crucial for developing effective hazard management strategies tailored to specific times of the year. |
ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas
https://ui.adsabs.harvard.edu/abs/2022EnvCh...900624F/abstract
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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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Data Collection |
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The study initiates with extensive data collection, encompassing geotechnical parameters such as soil composition, topography, precipitation patterns, temperature, and historical landslide occurrences. This foundational step ensures a comprehensive dataset for analysis. |
ARIMA and SPSS Statistics Based Assessment of Landslide Occurrence in Western Himalayas
https://www.researchgate.net/publication/364041729_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
https://www.sciencedirect.com/science/article/pii/S2405844023106323 |
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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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The highly susceptible zones cover the Chattogram district's hill ranges where active morphological processes (erosion and denudation) are dominant. 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. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
https://www.sciencedirect.com/science/article/pii/S2405844023106323 |
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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 extent of the study area |
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the study area and the size of the inventory. Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
https://www.sciencedirect.com/science/article/pii/S2405844023106323 |
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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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Among the models, LR showed the highest prediction rate and DRT showed the highest success rate. According to susceptibility zones, DRT is the more realistic model followed by LR. The maps can be applied at the local scale for landslide hazard management. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
https://www.sciencedirect.com/science/article/pii/S2405844023106323 |
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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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Logistic Regression (LR): |
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Interpretability:
LR provides a straightforward interpretation of coefficients, making it easier to understand the influence of each input variable on landslide susceptibility. This transparency is valuable for stakeholders and decision-makers.
Efficiency with Linear Relationships:
LR excels when relationships between input variables and landslide susceptibility are approximately linear. It is effective in capturing simple, linear patterns in the data.
Limitations:
Assumption of Linearity:
LR assumes a linear relationship between predictors and the log-odds of the event. If the true relationship is highly nonlinear, LR may not capture complex patterns adequately. |
GIS-based landslide susceptibility mapping using logistic regression, random forest and decision and regression tree models in Chattogram District, Bangladesh
https://www.sciencedirect.com/science/article/pii/S2405844023106323 |
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