Rainfall-induced landslide deformation is governed by both intrinsic geological conditions and external dynamic triggers. However, many existing predictive models rely primarily on rainfall inputs, which limits their interpretability and robustness. To address these shortcomings, this study introduces a group-based data augmentation method informed by displacement curve morphology and proposes a multi-slope predictive framework that integrates static geological attributes with dynamic triggering factors. Using monitoring data from 274 sites across China, the framework was implemented with a Temporal Fusion Transformer (TFT) and benchmarked against baseline models, including SVR, XGBoost, and LSTM models. The results demonstrate that group-based augmentation enhances the stability and accuracy of predictions, while the integrated dynamic–static TFT framework delivers superior accuracy and improved interpretability. Statistical significance testing further confirms consistent performance improvements across all groups. Collectively, these findings highlight the framework’s effectiveness for short-term landslide forecasting and underscore its potential to advance early warning systems.

Urban fire prevention is shifting from reactive response to proactive risk governance, yet current approaches often overlook risk-type heterogeneity, spatial dependencies, and underlying behavioral mechanisms, especially equitable risk distribution among vulnerable groups. To address this, this study integrates the Pressure–State–Response (PSR) model with environmental criminology theories (Routine Activity Theory (RAT) and Crime Pattern Theory (CPT)) to couple macro social causal chains with micro behavioral–spatial mechanisms. Using data from the digital urban management system of Shenzhen’s Guangming District in 2019, four fire risk event types are examined: electric bike charging violations (EB), unauthorized power wiring (PW), water heater misuse (WH), and aging gas pipelines (GP). Spatial error models explain 82–89% of the variance across fire risk event types, and spatial 5-fold cross-validation shows minimal performance decline (ΔR² = 0.03–0.08), confirming robust prediction without overfitting. Key findings include: (1) elderly proportion is significantly positively associated with WH and PW (coefficients = 2.64 and 3.06, p < 0.01); (2) restaurant density has a consistently positive association with all four risk types (coefficients = 0.24–0.60, p < 0.01); (3) functional diversity and connectivity exhibit dual patterns, showing negative associations with more visible, easily detectable violations (PW, GP) but positive relationships with relatively concealed behaviors (EB); (4) reported safety deficiencies display strong positive associations with all fire risk event types and can therefore serve as an effective early-warning indicator for broader fire risk. These results support risk-specific, equity-oriented prevention strategies that prioritize vulnerable groups and high-risk environments. The validated PSR–RAT/CPT framework provides a novel theoretical basis for targeted fire risk governance and advances safe, resilient, inclusive cities aligned with Sustainable Development Goal 11.