Predictive modeling study demonstrates high-accuracy climate risk categorization in urban environments, indicating potential to improve long-term municipal resilience planning.
Key Points
To develop and evaluate an integrated AI predictive analytics framework that combines data augmentation, deep feature extraction, and adaptive classification to assess urban climate resilience.
Integrated a Climate Change CycleGAN (CC-CycleGAN) for data augmentation with a Deep Neural Network (DNN) and an adaptive MycelialNet architecture.
Standardized and spatially analyzed Air Quality Index (AQI) measurements, ecological vulnerability metrics, and environmental datasets across 15 Chinese prefectural cities.
Evaluated model performance using the Structural Similarity Index (SSIM), mean absolute error on monthly precipitation sequences, and classification metrics including AUC.
CC-CycleGAN data augmentation improved spatial fidelity by 18.4% (SSIM), reduced monthly precipitation-prediction bias by 21.6% (mean absolute error), and increased data diversity by 32% (k-nearest-neighbour coverage).
The integrated DNN–MycelialNet model achieved 94.8% accuracy, 93.6% precision, 94.1% recall, a 94.3% F1-score, and an AUC of 0.962, outperforming baseline models by 12–17% while reducing overfitting by 23%.
Projected 2035 climate scenarios categorized 4 cities as High-Risk (AQI > 200, ecological vulnerability > 0.75), 7 as Moderate-Risk (AQI 100–200, vulnerability 0.40–0.75), and 4 as Low-Risk (AQI < 100, vulnerability < 0.40).