The research, published in Frontiers in Environmental Engineering, benchmarked four widely used machine learning and deep learning architectures on a publicly available operational dataset from a municipal wastewater treatment plant in Melbourne, Australia. The team compared a feedforward neural network, a recurrent neural network, a one-dimensional convolutional neural network and the gradient-boosted tree algorithm XGBoost, all trained on identical data splits and feature sets. Their verdict is striking: a comparatively simple tree-based model matched or beat the deep learning alternatives while consuming between 98 and 99 percent less energy and emitting roughly 65 to 100 times less carbon dioxide equivalent than the recurrent network.

The motivation reaches far beyond one Australian plant. Globally, around 380 billion cubic metres of wastewater were generated annually as of 2015, and more recent estimates suggest 332 billion cubic metres of household wastewater alone in 2024, a figure expected to double by 2050 as cities expand. Only about 56 percent of household wastewater is currently treated for safe discharge, leaving a vast management gap. Treatment plants are increasingly being reframed as resource recovery facilities: anaerobic digestion of the organic carbon in wastewater can substitute for 40 to 76 percent of a plant’s electrical energy, while nutrient recovery technologies could capture up to 15 to 20 percent of the world’s phosphorus and nitrogen requirements from wastewater streams.

The stakes of getting these forecasts right are both economic and environmental. Reliable COD predictions can guide feed allocation to anaerobic digestion and fermentation processes that yield volatile fatty acids, the critical intermediates for biochemical and biofuel production, and polyhydroxyalkanoates, sustainable alternatives to petrochemical plastics. TN forecasts, meanwhile, can inform nutrient recovery, aeration control and compliance management. Untreated nitrogen released into water bodies drives eutrophication, the algal overgrowth that depletes oxygen, and nitrogen-removal processes themselves can emit nitrous oxide, a greenhouse gas with a global warming potential roughly 298 times that of carbon dioxide.

To build their comparison, the researchers started with 1,382 operational records containing sixteen variables. Data cleaning, variance screening and standardisation were fitted only on the training subset and then applied unchanged to validation and test data, a discipline designed to mimic how a model would behave on unseen plant data rather than under idealised retrospective conditions. Weather-related variables with near-zero variance, such as atmospheric pressure, total rainfall and average visibility, were discarded. A two-step feature screening combining Pearson, Spearman and distance correlations with ANOVA-based relevance ranking distilled the predictor space down to seven input features, used consistently across all four architectures to guarantee a fair fight.

The dataset was divided into 70 percent training, 15 percent validation and 15 percent test subsets, with hyperparameter selection guided by validation performance and regularisation plus early stopping applied to the neural models. The feedforward network used two hidden layers of 64 and 32 neurons; the recurrent network a single 32-unit layer; the convolutional network a one-dimensional layer with 32 filters and a kernel size of 3; and XGBoost 100 boosting trees with a maximum depth of 6 and a learning rate of 0.3. All neural models trained for 400 epochs with a batch size of 50, and the identical configuration was applied to both targets so that performance differences reflected the data rather than the setup.

For COD, the recurrent neural network captured temporal behaviour most effectively, achieving an R-squared of 0.71 and a root mean square error of 75.55 milligrams per litre, with points hugging the diagonal of the actual-versus-predicted plot and residuals more symmetrically clustered around zero than the feedforward baseline, which scored 0.65. XGBoost matched that test performance at 0.71 with the strongest generalisation, posting validation and test R-squared values of 0.72 and 0.71, though its near-perfect training fit of 0.99 signalled mild overfitting, a familiar trait of high-capacity tree ensembles on structured data. For Total Nitrogen, XGBoost was the clear front-runner with validation and test R-squared values of 0.59 and 0.63, outperforming the recurrent network at 0.56 and 0.53 and the other neural architectures, suggesting that nitrogen variability is governed more by nonlinear feature interactions than by sequence memory.

The convolutional network added little value over the feedforward baseline for either target, confirming that local convolutional filtering does not benefit low-dimensional tabular wastewater data. The moderate R-squared range of roughly 0.54 to 0.71 observed across all models is realistic for operational plant datasets, which typically lack dissolved oxygen, sludge retention time and explicit kinetic parameters, and the authors note that state-of-the-art wastewater modelling studies on real plant data usually land between 0.6 and 0.8. The ceiling here reflects missing process-state variables and inherent sensor noise rather than model inadequacy.

The study’s most consequential contribution is its sustainability audit. Using the CodeCarbon library, the team recorded electricity use, water consumption for data-centre cooling and carbon dioxide equivalent emissions for every model-target combination. For COD, XGBoost required only 0.000018 kilowatt-hours and emitted 0.0000129 kilograms of CO2-equivalent, compared with 0.001490 kilowatt-hours and 0.001063 kilograms for the recurrent network; for Total Nitrogen, XGBoost consumed 0.000028 kilowatt-hours against 0.002563 for the recurrent model. From these measurements the researchers derived an Environmental Cost Index, a Green Performance Index and a Triple Green Sustainability Score, with XGBoost topping the green rankings at 0.63 to 0.70 and 0.64 to 0.72 respectively while the recurrent network carried the heaviest environmental burden.

The broader context makes these small numbers matter. AI systems could be responsible for 32.6 to 79.7 million tons of CO2 emissions in 2025, data centres consumed about 4.4 percent of United States electricity in 2023, and under current trends they could approach 9 percent of global electricity demand by 2030. Because machine learning models are trained and redeployed repeatedly across facilities, the cumulative footprint of an energy-hungry architecture compounds. The authors’ conclusion reframes model selection for environmental engineering as a multi-criteria decision problem in which algorithmic greenness is a core technical consideration rather than an afterthought: when a lean tree ensemble predicts as well as a deep network, the conventional instinct toward architectural complexity is unjustified on both performance and sustainability grounds, and the future of wastewater analytics may belong to the models that do more with far less.

Subject of Research: Green machine learning model selection for predicting COD and Total Nitrogen in wastewater valorisation

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

Article References: Debnath, B., Pal, R. S. C., & Chattopadhyay, A. K. (2026). Green machine learning assisted analytics for wastewater valorisation: a case study of Melbourne, Australia. Frontiers in Environmental Engineering, 5, Article 1839957. https://doi.org/10.3389/fenve.2026.1839957

Image Credits: AI Generated

DOI: 10.3389/fenve.2026.1839957

Keywords: green machine learning, wastewater valorisation, XGBoost, recurrent neural network, chemical oxygen demand, total nitrogen, CodeCarbon, carbon footprint, resource recovery, circular economy, Melbourne wastewater dataset, sustainable AI