Abstract
Urban Ecological Resilience (UER) is essential for sustainable development, especially within ecologically sensitive regions such as China’s Yellow River Basin (YRB). Existing assessments of UER often encounter difficulties attributable to extensive regional boundaries and retrospective methodologies, thereby limiting their applicability in policymaking. To address these limitations, this study presents an innovative framework. Initially, 51 cities were classified into seven functional clusters based on ecological and industrial similarities. Subsequently, the UER for each cluster was quantified from 2010 to 2024 utilizing the Entropy Weight Method. Projections for UER from 2025 to 2027 were generated employing an XGBoost (eXtreme Gradient Boosting) model that integrates temporal features derived from historical data. The findings indicate a concerning decline in UER within the traditional heavy industry cluster, alongside fluctuating decreases in the Loess Plateau agriculture and conventional agriculture clusters. Model interpretations identify vulnerable cities and low-performing indicators, such as per capita water resources, environmental protection budgets, and industrial pollution, which are strongly correlated with these predicted declines. Conditional simulation demonstrates that targeted interventions aimed at these indicators have the potential to mitigate adverse trends. This comprehensive approach provides a quantitative, proactive tool for formulating specific strategies to enhance UER across diverse regions.
Funding
This research was funded by the Scientific Research Program of the Education Department of the Shaanxi Provincial Government, grant number 23JK0625, and the Natural Science Basic Research Program of Shaanxi, grant number 2025JC-YBQN-404.
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Fan, T., Li, X., Huang, C. et al. Predicting the future of urban ecological resilience in China’s Yellow River Basin: a machine learning approach.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-54737-0
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DOI: https://doi.org/10.1038/s41598-026-54737-0
Keywords
- predictive modelling
- extreme gradient boosting
- early warning
- regional ecological assessment
- countermeasure
- clustering analysis
Source: Ecology - nature.com
