Jinlin Teng

Urban greenway trails serve as a vital link between urban populations and the natural environment, playing a key role in enhancing quality of life and promoting physical and mental well-being. We propose an interpretable machine learning framework applied to 424 geotagged footprint images from the Bada Shanren Meihu Scenic Area in China. Our main findings are as follows: (1) The key factors influencing trail landscape preferences include the Water Visibility Index (WVI), Building Landscape Index (BVI), Freedom Index, and Greenery Visibility Index (GVI). (2) For WVI, SHAP values significantly increase around the 0.05 threshold. BVI has a critical threshold of 0.17, with a strong influence below it and a reduced effect above it. The Freedom variable shows an inverse relationship, with minimal contribution below 0.21 and a sharp increase above this threshold. GVI maintains high SHAP values at lower levels (GVI ≤ 0.66), but its predictive utility decreases at higher values. (3) Landscape preferences are significantly positively correlated with naturalness, wildness, WVI, and openness, with water landscapes being the strongest driver. In contrast, artificial factors, V_Low, and H_Purple significantly suppress preferences. This suggests that human intervention and certain color tones may reduce the attractiveness of the landscape.

The evaluation of urban noise suitability is crucial for urban environmental management. Efficient and cost-effective methods for obtaining noise distribution data are of great interest. This study introduces various machine learning methods and applies the Random Forest algorithm, which performed best, to investigate noise suitability in the central urban area of Nanchang City. The findings are as follows: 1.Machine learning algorithms can be effectively used for urban noise evaluation. The optimized model accurately reflects the noise suitability levels in Nanchang City. 2.The feature importance ranking reveals that population spatial distribution has the most significant impact on urban noise, followed by distance to water bodies and road network density. These three features significantly influence the assessment of urban noise suitability and should be prioritized in noise control measures. 3.The weakly suitable noise areas in Nanchang’s central urban region are primarily concentrated on the east bank of the Ganjiang River, making this a key area for noise management. Overall, the Unsuitable, Slightly suitable, Moderately suitable, Relatively suitable, and Height suitable areas constitute 9.38%, 16.03%, 28.02%, 33.31%, and 13.25% of the central urban area, respectively. 4.The SHAP model identifies the top three features in terms of importance, showing that different values of feature variables have varying impacts on noise suitability. This study employs data mining concepts and machine learning techniques to provide an accurate and objective assessment of urban noise levels. The results offer scientific decision-making support for urban spatial planning and noise mitigation measures, ensuring the healthy and sustainable development of the urban environment.