p.6
Model Evaluation Metrics and Performance
What was the notable performance gap observed in the models?
SVM displayed a notable performance gap compared with RF, AdaBoost, and XGBoost.
p.2
Ecological Factors Influencing Ecotourism
What key factors are considered in assessing the suitability of ecotourism?
Key factors include soil and water conservation potential, NDVI, slope, and relief.
p.5
Model Evaluation Metrics and Performance
Which model had the highest accuracy according to the evaluation metrics?
XGBoost had the highest accuracy at 0.9207.
p.1
Ecological Factors Influencing Ecotourism
What key ecological factors are considered in the ecotourism suitability assessment framework?
Soil and water conservation potential and NDVI, which reflect the ecological environment's carrying capacity.
p.12
Sustainable Development Principles in Ecotourism
What role does land evaluation play in sustainable regional development?
Land evaluation contributes to sustainable regional development by promoting scientific planning, rational use of land resources, and the healthy development of ecotourism, leading to coordinated environmental protection and economic growth.
p.8
Geographic Data Reliability in Ecotourism
How do unsuitable zones in Cili differ from suitable zones?
Unsuitable zones in Cili are larger due to flat topography that has developed into residential areas and farmland, limiting ecotourism resources.
p.13
Spatial Distribution of Ecotourism Resources
What does the cartographic representation of predicted outcomes show?
It delineates the spatial configuration of preexisting ecotourism destinations and identifies prospective ecotourism resources.
p.3
Machine Learning Algorithms in Ecotourism
What are the characteristics of the AdaBoost algorithm?
Robustness, adaptability, generalizability, and effective data fitting even with complex data distributions.
p.6
Model Evaluation Metrics and Performance
Which model had the best performance according to the ROC curve and AUC calculations?
XGBoost had the best performance, followed by AdaBoost, RF, and SVM.
p.10
Feature Importance in Machine Learning Models
What are the top three features influencing ecotourism development according to the model's average ranking?
Temperature, precipitation, and soil conservation capacity, accounting for 15.44%, 13.69%, and 13.83% respectively.
p.6
Model Evaluation Metrics and Performance
Which model exhibited superior performance under 10-fold cross-validation?
XGBoost exhibited superior performance compared to RF, AdaBoost, and SVM.
p.2
Sustainable Development Principles in Ecotourism
What is central to ecotourism and essential for constructing an assessment framework for ecotourism suitability?
The principle of sustainable development is central to ecotourism and essential for constructing an assessment framework for ecotourism suitability.
p.5
Research Framework for Ecotourism Suitability Assessment
What was the outcome of the mutual information method analysis?
All the features exhibited a positive mutual information estimate, suggesting their relevance to the labels.
p.9
Sustainable Development Principles in Ecotourism
What characteristics make certain zones well-suited for future ecotourism activities?
High vegetation cover, excellent soil and water conservation capacity, and unique flora and fauna make these zones well-suited for ecotourism.
p.9
Ecological Factors Influencing Ecotourism
What pattern do unsuitable zones in Sangzhi exhibit?
The unsuitable zones in Sangzhi exhibit a northeast-southwest-oriented strip-like pattern influenced by the local geological environment.
p.9
Ecological Factors Influencing Ecotourism
How does the topography of Cili affect its suitability for ecotourism?
Cili has larger unsuitable zones due to its flat topography, which has developed into residential areas and farmland with limited ecotourism resources.
p.11
Feature Importance in Machine Learning Models
How do machine learning techniques differ from traditional methods in determining feature importance for ecotourism?
Machine learning techniques derive feature importance based on inherent attributes of analyzed sample data without human intervention, while traditional methods often rely on expert scoring or AHP, which can introduce subjectivity.
p.1
Research Framework for Ecotourism Suitability Assessment
What is the purpose of the data acquisition and preprocessing phase in the ecotourism suitability assessment framework?
To consolidate geospatial data from different sources and create feature matrices for modeling.
p.6
Machine Learning Algorithms in Ecotourism
Why might tree-based models perform better in capturing spatial nuances?
Tree-based models possess greater learning capacities, making them better equipped to capture intricate spatial nuances in the data.
p.2
Research Framework for Ecotourism Suitability Assessment
How does the framework for ecotourism suitability planning approach local planning?
The framework utilizes suitability probabilities applied to individual raster cells, offering a high-resolution approach to local ecotourism planning.
p.2
Machine Learning Algorithms in Ecotourism
What is the main advantage of the Random Forest (RF) algorithm in machine learning?
The RF algorithm benefits from the bagging technique, which improves prediction accuracy and increases model diversity using random sampling.
p.2
Machine Learning Algorithms in Ecotourism
What does XGBoost focus on when adding new decision trees?
XGBoost focuses on reducing the prediction residuals of the previous tree to improve overall prediction accuracy.
p.11
Impact of Land Policies on Ecotourism Development
How can evaluation findings influence land use planning for ecotourism?
Evaluation findings can identify areas suitable for ecotourism and those needing protection, guiding the formulation of precise land policies and improving land use planning.
p.10
Ecological Factors Influencing Ecotourism
How does relief height contribute to ecotourism resources?
Increased relief height contributes to the formation of unique geological features, supporting the development of ecotourism resources with exceptional scenic value.
p.10
Ecological Factors Influencing Ecotourism
What significance does forest age have in ecotourism?
A higher forest age indicates greater potential for ecotourism resources focused on tall vegetation, enhancing opportunities for visitors to explore plant diversity.
p.8
Impact of Land Policies on Ecotourism Development
What types of land use are found in the northern part of Yongding?
The northern part of Yongding is characterized by urban land use with a significant amount of farmland on the periphery, including permanent basic farmland essential for food security.
p.8
Geographic Data Reliability in Ecotourism
What influences the unsuitable zones in Sangzhi?
The unsuitable zones in Sangzhi exhibit a northeast-southwest-oriented strip-like pattern influenced by the unique topography created by the local geological environment.
p.3
Machine Learning Algorithms in Ecotourism
What is the main function of the Support Vector Machine (SVM) algorithm?
To classify data by identifying an optimal hyperplane.
p.6
Model Evaluation Metrics and Performance
What does the confusion matrix visualize in the context of model performance?
The confusion matrix visualizes the model’s classification performance.
p.9
Ecological Factors Influencing Ecotourism
What types of land use dominate the urban areas and outskirts of the Wulingyuan scenic area?
Residential buildings, water conservation land, and permanent basic farmland dominate these areas, which do not support ecotourism development.
p.6
Spatial Distribution of Ecotourism Resources
What does the spatial variability in model fit indicate?
The spatial variability in model fit was consistent with the actual growth patterns of ecotourism in the region.
p.5
Research Framework for Ecotourism Suitability Assessment
What methods were integrated for feature selection in ecotourism suitability assessment modeling?
The variance threshold and mutual information methods were integrated for feature selection.
p.5
Research Framework for Ecotourism Suitability Assessment
What did the variance threshold test reveal about the features?
The variance threshold test revealed no redundant features with a variance of zero.
p.9
Impact of Land Policies on Ecotourism Development
What is the significance of permanent basic farmland in the northern part of Yongding?
Permanent basic farmland is essential for food security in China and is located on the periphery of the urban area of Zhangjiajie.
p.1
Geographic Data Reliability in Ecotourism
From where was the geographic location data for ecotourism attractions sourced?
The Third Survey and Mapping Institute of Hunan Province.
p.7
Ecological Factors Influencing Ecotourism
What urban area was found to be unsuitable for ecotourism development due to human activity?
The urban area of Zhangjiajie was found to be unsuitable for ecotourism development due to the prevalence of human activity.
p.11
Impact of Land Policies on Ecotourism Development
What is the impact of land policies on ecotourism development?
Land policies regulate ecotourism and can either hinder or support its development, influencing land use planning and the formulation of effective ecotourism policies.
p.7
Ecological Factors Influencing Ecotourism
Which part of Zhangjiajie showed high suitability for ecotourism development?
The southern part of the urban area of Zhangjiajie exhibited high suitability for ecotourism development.
p.11
Model Evaluation Metrics and Performance
What techniques were used to improve the reliability of the machine learning model in the study?
The study utilized learning curves, grid search, cross-validation, and regularization techniques to prevent overfitting and enhance model applicability.
p.7
Spatial Distribution of Ecotourism Resources
What popular tourist destinations are located in the southern part of Zhangjiajie?
Popular tourist destinations include Tianmen Cave, Ghost Valley Trail, and Glass Viewing Platform.
p.5
Model Evaluation Metrics and Performance
Which model exhibited superior performance under ten-fold cross-validation?
XGBoost exhibited superior performance compared to the remaining three types of models.
p.10
Ecological Factors Influencing Ecotourism
Why is adequate precipitation important for ecotourism?
Adequate precipitation is essential for vegetation growth and the development of unique ecological landscapes, which are crucial for ecotourism initiatives.
p.1
Research Framework for Ecotourism Suitability Assessment
What does model training involve in the context of ecotourism suitability assessment?
Processing sample points and training the machine learning model using extracted raster layer values.
p.5
Model Evaluation Metrics and Performance
What visualization tools were used to understand the model's performance?
The confusion matrix and ROC curves were used to visualize the model's performance.
p.10
Sustainable Development Principles in Ecotourism
What role does soil conservation play in sustainable ecotourism?
Soil conservation affects a region's environmental carrying capacity, minimizing the negative effects of tourism on the environment.
p.11
Ecological Factors Influencing Ecotourism
What is the significance of higher forest age in ecotourism resources?
A higher forest age indicates greater potential for ecotourism resources focused on tall vegetation, allowing visitors to immerse themselves in nature and explore plant diversity.
p.5
Model Evaluation Metrics and Performance
What evaluation metrics were selected to assess the model's classification performance?
Accuracy, precision, recall, F1, and area under the curve (AUC) were selected as evaluation metrics.
p.7
Model Evaluation Metrics and Performance
How did the predictions of the four models perform in the Wulingyuan District?
The predictions of the four models exhibited a high degree of consistency in the Wulingyuan District.
p.12
Impact of Land Policies on Ecotourism Development
What is the significance of the dual emphasis on conservation and sustainable development in land use policy?
The dual emphasis on conservation and sustainable development aims to balance ecological protection with the expansion of eco-tourism activities, preserving natural habitats while improving local economic wellbeing.
p.12
Future Directions for Ecotourism Research
What future research directions are suggested for improving ecotourism suitability evaluation?
Future research should explore incorporating transfer learning to reuse models or samples from different domains and conduct a global-scale evaluation of ecotourism suitability.
p.3
Machine Learning Algorithms in Ecotourism
How does SVM handle non-linearly separable data?
By introducing a kernel function to map the data to a higher-dimensional space.
p.1
Geographic Data Reliability in Ecotourism
What is essential for ensuring the reliability of a machine learning model's training in ecotourism?
Precise geographic location data for ecotourism attractions.
p.9
Research Framework for Ecotourism Suitability Assessment
What areas are considered moderately suitable for ecotourism?
Moderately suitable areas are primarily concentrated around the periphery of human-intensive areas and mountainous regions, offering protective functions and development potential.
p.11
Geographic Data Reliability in Ecotourism
What role does data reliability play in machine learning for ecotourism?
Reliable training data is crucial for minimizing variations in models and improving their strength and applicability in ecotourism predictions.
p.2
Feature Importance in Machine Learning Models
What components make up the objective function of XGBoost?
The objective function consists of a loss function and a regularization term.
p.7
Model Evaluation Metrics and Performance
What approach was used to verify the predictive results in the study?
The average prediction across the four models was computed to verify the predictive results.
p.13
Impact of Land Policies on Ecotourism Development
What measures has the government instituted to conserve ecologically sensitive regions?
The government has instituted land use policies such as ecological protection red lines and restrictions on urban expansion.
p.3
Machine Learning Algorithms in Ecotourism
What are the key advantages of the XGBoost algorithm?
Outstanding predictive performance, scalability, interpretability, support for parallel computing, and automatic handling of missing values.
p.9
Feature Importance in Machine Learning Models
How do machine learning algorithms determine the importance of features in tree models?
The importance of a feature is determined by the frequency with which it is chosen as a split point or the degree of information gain resulting from the split.
p.1
Research Framework for Ecotourism Suitability Assessment
What are the three main components of the research framework for assessing ecotourism suitability?
Data acquisition and preprocessing, model training, and model evaluation and prediction.
p.2
Model Evaluation Metrics and Performance
What does the regularization term in XGBoost control?
The regularization term controls model complexity to prevent overfitting and improve model generalization ability.
p.4
Ecological Factors Influencing Ecotourism
What does a correlation value of 0.91 between slope and relief suggest?
It aligns with fundamental geographic principles, indicating a strong relationship.
p.12
Machine Learning Algorithms in Ecotourism
What are the limitations of using machine learning for evaluating ecotourism suitability?
The effectiveness of machine learning methods relies on having a sufficient number of labeled samples; limitations may arise if training samples are insufficient or lacking.
p.3
Machine Learning Algorithms in Ecotourism
What are the first- and second-order gradient statistics of the loss function in the XGBoost algorithm?
g i = ∂ ˆ y ( t − 1 ) i l ( y i , ˆ y ( t − 1 )) and h i = ∂ 2 ˆ y ( t − 1 ) i l ( y i , ˆ y ( t − 1 ))
p.10
Ecological Factors Influencing Ecotourism
How does temperature affect ecotourism destinations?
Temperature influences the attractiveness of ecotourism destinations, especially in regions like Zhangjiajie, which offers a comfortable environment during summer.
p.8
Spatial Distribution of Ecotourism Resources
Where are the zones with high suitability for ecotourism primarily located?
The zones with high suitability are mostly located in the central region of Wulingyuan, southern and southeastern regions of Yongding, northwestern, western, and southwestern regions of Cili, and northern, northwestern, and eastern regions of Sangzhi.
p.8
Ecological Factors Influencing Ecotourism
What factors contribute to the high suitability of areas in the southern part of Yongding?
The high suitability is related to the presence of Tianmen Mountain National Forest Park and the transitional zone between the basin topography of the urban area of Zhangjiajie and the southern Tianmen Mountain.
p.7
Spatial Distribution of Ecotourism Resources
Which regions were identified as having high suitability for ecotourism?
High suitability zones were located in central Wulingyuan, southern and southeastern Yongding, and other specified regions.
p.13
Model Evaluation Metrics and Performance
What was the AUC of the SVM model for the training data?
The AUC of the SVM model was 0.9567.
p.3
Machine Learning Algorithms in Ecotourism
What does the equation f ( x ) = sign ( N ∑ n = 1 α n · G n ( x ) ) represent in AdaBoost?
It represents the trained strong learner based on the weighted sum of weak learners.
p.7
Ecological Factors Influencing Ecotourism
What natural features characterize the southern part of Zhangjiajie?
The area is characterized by mountainous terrain, lush vegetation, rivers, and diverse natural scenery.
p.1
Model Evaluation Metrics and Performance
Why is it important to combine predictions from multiple machine learning models?
To mitigate the risk of relying on a single model and enhance prediction accuracy.
p.5
Model Evaluation Metrics and Performance
How did AdaBoost's overall error rate compare to SVM's?
The overall error rate of AdaBoost was slightly higher than that of SVM, but the difference was not substantial.
p.13
Research Framework for Ecotourism Suitability Assessment
Which model performed the best in predicting ecotourism suitability?
XGBoost performed the best with an AUC of 0.9884.
p.12
Geographic Data Reliability in Ecotourism
How does the China Spatial Planning Observation Network (CSPON) enhance land policy implementation?
CSPON provides real-time monitoring and early warning systems for national and regional spatial plans, enhancing the scientific rigor and effectiveness of spatial planning and management.
p.7
Ecological Factors Influencing Ecotourism
What is the significance of the Wulingyuan District in terms of ecotourism?
The Wulingyuan District is generally well suited for ecotourism due to its unique natural and cultural tourism resources.
p.1
Sustainable Development Principles in Ecotourism
What principle is central to ecotourism and must be considered in the assessment framework?
The principle of sustainable development.
p.4
Research Framework for Ecotourism Suitability Assessment
What methods were integrated for feature selection in ecotourism suitability assessment modeling?
The variance threshold and mutual information methods were integrated.
p.12
Research Framework for Ecotourism Suitability Assessment
What was the main finding of the empirical study conducted in Zhangjiajie, China?
The study demonstrated the effectiveness of using machine learning methods for regional ecotourism suitability evaluation, achieving objective results without human interference in determining factor weights.
p.3
Machine Learning Algorithms in Ecotourism
What is the main principle behind the AdaBoost algorithm?
To combine multiple weak classifiers into a single robust classifier by adjusting weights assigned to samples and classifiers.
p.4
Ecological Factors Influencing Ecotourism
What was the correlation value between precipitation and elevation in the Zhangjiajie Mountain Area?
The correlation value was 0.87, indicating a strong connection.
p.8
Spatial Distribution of Ecotourism Resources
What is the distribution pattern of moderate suitability zones?
Moderate suitability zones exhibit a distribution pattern similar to highly suitable zones, primarily concentrated around their periphery.
p.3
Machine Learning Algorithms in Ecotourism
What is the optimized objective function in the context of the provided equation?
∼ L ( t ) = I ∑ i = 1 ( g i f t ( x i ) + 1/2 h i f 2 t ( x i )) + Ω ( f k )
p.7
Spatial Distribution of Ecotourism Resources
What renowned ecotourism destination is located in the Wulingyuan District?
The Wulingyuan Scenic Spot is a renowned ecotourism destination in the Wulingyuan District.
p.8
Ecological Factors Influencing Ecotourism
What characteristics define the highly suitable zones in Cili?
The highly suitable zones in Cili are characterized by mountainous terrain, high vegetation cover, strong soil and water conservation capacity, and outstanding aesthetic landscape value.
p.13
Research Framework for Ecotourism Suitability Assessment
What percentage of the total area was classified as highly suitable for ecotourism?
19.34% of the total area was classified as highly suitable for ecotourism.
p.13
Machine Learning Algorithms in Ecotourism
What is a viable option for model transfer in regions with insufficient training samples?
Transfer learning is a viable option for model transfer in such regions.
p.3
Machine Learning Algorithms in Ecotourism
What is the significance of the bias term 'b' in the SVM equation?
It adjusts the position of the hyperplane in the feature space.
p.12
Impact of Land Policies on Ecotourism Development
How can land use be organized to support ecotourism activities?
Land use can be organized to allow tourism to coexist harmoniously with the natural environment by considering the needs of ecotourism activities and developing targeted land and soil remediation plans to address illegal land use.
p.4
Ecological Factors Influencing Ecotourism
Which two features had the highest correlation value of 0.94?
Biodiversity and ALSV (Aboveground Live Stock Value).
p.4
Research Framework for Ecotourism Suitability Assessment
What did the variance threshold test reveal about the features?
It revealed no redundant features with a variance of zero.
p.13
Impact of Land Policies on Ecotourism Development
What are some constraints faced by potential ecotourism locations?
Constraints include transportation infrastructure, topography, tourism-related amenities, and undeveloped policies.
