Derivation of marine water quality criteria for copper based on artificial neural network model

The water chemical effects of copper have been a focus in the study of water quality criteria (WQC). Currently, multiple regression models are commonly used to quantitatively describe the impact of environmental factors on Cu toxicity in WQC studies. However, the influence of species-specific effects may consequently lead to poor prediction results of the regression models in practical application. For this issue, a backpropagation neural network (BPNN) model optimized using a genetic algorithm was developed in this study. The results showed when pooled data of given taxonomic groups were used, the BPNN mixed models had higher Adj.R² for five out of seven groups in the predicted toxicity values compared to the MNLR mixed models. When using species-specific models, the BPNN model still showed higher predictive performance. Further comparison of the two models for the species M. galloprovincialis revealed that, in addition to the good predictive performance of the BPNN models, the pre-set species codes of different species in the taxonomic group for the BPNN mixed model also reduced the impact of species-specific effects among species. Finally, the WQCs under different water quality parameter ranges were obtained using predicted toxicity values from mixed BPNN and MNLR models. The short-term WQC range for common water quality parameters (salinity: 25-30 ppt, DOC: 0.5-2.5 mg/L) obtained from the BPNN mixed model in natural marine environments was 1.6-4.41 μg/L, which aligns with guidance values provided by major global institutions, demonstrating the feasibility of applying the BPNN mixed model to WQC derivation. This study aims to provide valuable references for future research on WQC.