SUVA-Based Modelling of THMFP Under Ozonation Using Regression and ANN Approaches

Drinking-water treatment systems must effectively control natural organic matter (NOM), a major precursor of regulated disinfection by-products (DBPs). Specific ultraviolet absorbance (SUVA) is widely used as an operational surrogate for NOM aromaticity and hydrophobicity; however, ozonation and subsequent filtration can disrupt the linear relationship between SUVA and trihalomethane formation potential (THMFP). This study evaluates whether SUVA can reliably predict THMFP under two ozonation configurations frequently applied in drinking-water treatment: pre-ozonation prior to coagulation–filtration and final ozonation following filtration. Experimental data were analyzed using conventional linear regression and artificial neural network (ANN) models, with SUVA employed as the sole predictor variable. Across all treatment configurations, reductions in SUVA were consistently more pronounced than corresponding decreases in THMFP, indicating a decoupling between chromophoric loss and chlorine-reactive precursor dynamics under ozonation-dominated conditions. Linear regression models exhibited only moderate predictive performance (R2 = 0.63–0.76), reflecting the limitations of proportional surrogate-based approaches when NOM undergoes oxidative and adsorptive transformation. In contrast, single-parameter ANN models captured the nonlinear SUVA–THMFP relationship with substantially higher accuracy across both pre- and final-ozonation regimes (R2 = 0.88–0.99), successfully resolving process-dependent patterns embedded within optically compressed SUVA signals. These findings demonstrate that, although SUVA alone cannot linearly represent the multistep transformation of NOM during ozonation and adsorption, it retains process-relevant structure information on DBP precursor reactivity that can be effectively extracted using nonlinear modelling. The results highlight the potential of integrating ANN-driven tools into advanced monitoring and DBP-control strategies in modern drinking-water treatment systems.