Green machine learning assisted analytics for wastewater valorisation: a case study of Melbourne, Australia

Wastewater valorisation inherently benefits from predictive models that are accurate, generic, robust and environmentally sustainable. Benchmarked on a publicly available municipal wastewater dataset from Melbourne, Australia, characterised by temporal variability and multivariate complexity, we present a Green Machine Learning (GML) framework to predict Chemical Oxygen Demand (COD) and Total Nitrogen (TN), two variables directly linked to carbon and nutrient recovery pathways. Four widely used machine learning and deep learning models - FFNN, RNN, CNN, and XGBoost - were benchmarked to identify the most optimised approach, defined here as the model providing the best overall balance between predictive accuracy, generalisation, and computational environmental cost rather than the highest training performance alone. XGBoost and RNN emerged as the most optimised algorithmic pathways. RNN captured the temporal behaviour most effectively for COD, achieving an R 2 of 0.71 and an RMSE of 75.55 mg/L, while XGBoost showed the strongest generalisation, with validation/test R 2 values of 0.72/0/71, respectively. For TN, XGBoost was the best-performing model, with validation/test R 2 = 0.59/0.63, outperforming RNN (0.56/0.53) and the other neural architectures. Sustainability analysis using CodeCarbon showed that XGBoost required only 0.000018 kWh and emitted 0.0000129 kg CO 2 -eq for COD, compared to 0.001490 kWh and 0.001063 kg CO 2 -eq for RNN; for TN, XGBoost consumed 0.000028 kWh, compared to 0.002563 kWh for RNN. These results conclusively show that XGBoost delivers comparable or better predictions at 98%–99% lower computational carbon cost, while RNN remains the strongest neural network candidate for COD-related dynamics.