Integrated Multi-Hazard Risk Assessment for Delhi with Quantile-Regressed LightGBM and SHAP Interpretation

Rapid urbanization, environmental degradation and climate variability are intensifying the exposure of urban populations to multiple, interacting hazards in megacities. In India’s capital, Delhi, extreme heat, worsening air quality and flood-related stress overlap in impacted areas, exacerbated by high population density in low-lying zones and extensive built-up cover. This study develops an integrated spatial framework for assessing relative multi-hazard risk potential in Delhi by combining remote sensing, climate reanalysis, land use and demographic datasets into a predictive modeling system to support urban resilience planning. A comprehensive suite of twenty-two predictors representing thermal stress, air quality, surface indices, topography, hydrology, land use land cover (LULC), and demographic data was derived from diverse Earth observation sources. A cloud-native workflow leveraging Google Earth Engine (GEE) and Python 3 harmonized these predictors to train a Light Gradient Boosting Machine (LightGBM) model with five-fold spatial cross-validation. Quantile regression was used to estimate lower (P10) and upper (P90) predictive bounds, which are interpreted here as empirical predictive intervals around the modeled risk surface rather than as a strict separation of different uncertainty types, while SHapley Additive exPlanations (SHAP) decomposed the non-linear contributions of individual features. The model achieved predictive accuracy (R 2 = 0.98, MAE = 0.01), with residuals centered near zero and consistent performance across spatial folds, demonstrating strong generalizability. Road density (63.4%) and population density (25.9%) emerged as the primary predictors of the modeled risk surface, followed by building density and NO 2 concentration. Conversely, vegetation cover (NDVI) functioned as a critical mitigating buffer. Spatial risk maps identified persistent high-risk clusters in eastern and northeastern Delhi, coinciding with dense transport networks and industrial zones. The integrated P90 mapping framework provides spatially explicit and uncertainty-aware information on relative multi-hazard risk potential to guide targeted interventions, such as transport corridor mitigation and urban greening in Delhi and other rapidly urbanizing cities.