Multi-stage robust groundwater quality assessment framework and key pollution sources identification under multiple uncertainties

Study region Owing to shallow burial depth, superior permeability of the aeration zone, and limited protective capacity, the quality of groundwater is notably influenced by human activities in Luoyang City alongside the Yiluo River, which is the largest primary tributary in the Yellow River Basin (YRB). Study focus To accurately identify the criteria or parameters that influence groundwater quality and revealing associated risks, we propose multi-stage stochastic assessment, risk analysis and pollution source identification framework for groundwater quality assessment (GQA) that simultaneously considers multiple uncertainties (temporal variation of water quality, sampling time, water quality data series, model parameters) in GQA. The groundwater quality is assessed by the probabilistic ranking, quantified risk and complexity analysis based on fuzzy entropy. New hydrological insights for the region (1) when considering uncertainties in the performance values (PVs) of indicators and criteria weights (CWs), groundwater well (No.4, GW4) obtains the optimal ranking with a probability of approximately 90% under stochastic CWs, while the risk of assessment error fluctuates within the range of 1.23–1.26%. (2) the central weight vector assists managers in identifying potential pollution sources and key criteria influencing groundwater quality. (3) the fuzzy entropy-based sub-decision system describes and quantifies the fuzzy complexity of the GQA in the context of different assessment methods. (4) robustness analysis under different parameter scenarios shows that the parameter of entropy increases the uncertainty in the ranking information associated with GQA.