Understanding multi-hazard risk dynamics between land use, climate extremes and water quality leveraging artificial intelligence

• Multi-hazard risk assessment to support the Water Framework Directive in Veneto. • AI-based framework links climate and land use to key water quality indicators. • Explainable AI enhances transparency, revealing compound effects on water quality. • Heat, drought, and land use combine to accelerate water quality impacts. • Natural features help buffer climate extremes’ impacts on ecosystems. This study develops an interpretable machine learning framework by integrating Random Forest (RF) classifiers with SHapley Additive exPlanations (SHAP) to analyze the interactions between land use, climate extremes, and water quality at a high spatial resolution in Veneto Region, Italy. The framework predicts annual impacts (2010–2022) on water quality, supporting the evaluation of ecological status in line with the Water Framework Directive. Three RF models achieved accuracies of 0.87, 0.81, and 0.85 in predicting impacts on physico-chemical elements, specific pollutants, and biological alterations, respectively. SHAP values helped interpret the model outputs by identifying key variables and their contribution to impacts on water quality. Temperature extremes, especially when combined with drought, emerged as key drivers of degradation. Natural features (e.g., vegetation, river naturality, permeable soils) buffered impacts, while intensive anthropogenic land use amplified negative effects under extreme climate conditions. This approach supports decision-making by identifying multi-hazard risks and emphasizing nature-based solutions, ecosystem restoration, and strategic land use for climate-resilient water management.