Existing research on automatic river network classification methods has difficulty scientifically quantifying and determining feature threshold settings and evaluating weights when calculating multi-indicator features of the local and overall structures of river reaches. In order to further improve the accuracy of river network classification and evaluate the feature weight, this paper proposes an automatic grading method for river networks based on ensemble learning in CatBoost. First, the graded river network based on expert knowledge is taken as the case; with the support of the existing case results, a total of eight features from the semantic, geometric, and topological aspects of the river network were selected for calculation. Second, the classification model, obtained through learning and training, was used to calculate the classification results of the main stream and tributaries of the river reach to be classified. Furthermore, the main stream river reaches were connected, and the main stream rivers at different levels were hierarchized to achieve river network classification. Finally, the Shapley Additive explanation (SHAP) framework for interpreting machine learning models was introduced to test the influence of feature terms on the classification results from the global and local aspects, so as to improve the interpretability and transparency of the model. Performance evaluation can determine the advantages and disadvantages of the classifier, improve the classification effect and practicability of the classifier, and improve the accuracy and reliability of river network classification. The experiment demonstrates that the proposed method achieves expert-level imitation and has higher accuracy for identifying the main stream and tributaries of river networks. Compared with other classification algorithms, the accuracy was improved by 0.85–5.94%, the precision was improved by 1.82–9.84%, and the F1_Score was improved by 0.8–5.74%. In this paper, CatBoost is used for river network classification for the first time, and SHAP is used to explain the influence of characteristics, which improves the accuracy of river network classification and enhances the interpretability of the classification method. By constructing a reasonable hierarchy, a better grading effect can be achieved, and the intelligence level of automatic grading of river networks can be further improved.

Scenic area attractiveness is a core factor in urban tourism development. Developments in social media and multi-source spatiotemporal data provide a basis for studying complex tourist behaviors, overcoming the limitations of traditional interview survey data. This study combines point of interest (POI), mobile signaling, and microblog check-in data to analyze scenic area popularity in Dali using kernel density analysis, hotspot analysis, and gravity models. It also uses ROST-CM6 to perform sentiment analysis on microblog check-in and text data to obtain tourist satisfaction, and combines the popularity and satisfaction to assess scenic area attractiveness. Additionally, GeoDetector is used to examine the impact of subjective human factors, objective factors of the attractions themselves, and the number of POI facilities around the attractions on the scenic area attractiveness in Dali. We obtained several key findings. First, the distribution of scenic areas in Dali City showed a two-center, multi-point pattern, including two core scenic areas (i.e., Dali Ancient City and Xizhou Ancient Town) and numerous scattered areas. Second, the majority of scenic areas in Dali City were more active in the daytime than at night, whereas Dali Ancient City was most active at night. Tourists in Dali City mostly came from Yunnan Province, neighboring provinces, and economically developed coastal regions. Third, a text-based sentiment analysis revealed numerous high-frequency adjectives reflecting positive sentiment, indicating high scenic area satisfaction. Fourth, the number of internal POIs had the greatest effects on scenic area popularity and attractiveness. Specifically, the more POIs, the more popular and attractive the scenic area. The interactive decision-making power of various factors was greater than the decision-making power of individual factors. These findings provide insight into the determinants of scenic area satisfaction, providing a basis for the development of urban tourism.