Author Identifier (ORCID)
Hoda Khoshvaght: https://orcid.org/0000-0001-8766-419X
Amir Razmjou: https://orcid.org/0000-0002-3554-5129
Mehdi Khiadani: https://orcid.org/0000-0003-1703-9342
Abstract
Accurate prediction of raw wastewater characteristics can reduce laboratory costs, enable optimized chemical dosing, and improve the overall efficiency of wastewater treatment plant (WWTP) operations. This study evaluates machine learning (ML) models as a fast, sustainable, and cost-effective alternative for predicting Biochemical Oxygen Demand (BOD) and Ammonia Nitrogen (NH4+-N) in raw wastewater. Seven ML algorithms, including tree-based, neural, and regression approaches, were trained using standard wastewater indicators, including organic load, nutrient levels, and pH, with hyperparameters tuned via grid search cross-validation. Model performance was evaluated using correlation-based (R2) and error-based metrics (RMSE, MAE, MAPE) to ensure a comprehensive assessment. This study investigates ML-based wastewater prediction using real-world weekly sampling data, in contrast to the high-frequency datasets that dominate existing research. Additionally, data distribution shifts, preprocessing techniques, and splitting strategies were evaluated, with the Smoothness Index used to assess consistency and temporal stability. Despite the limitations of longer sampling intervals, MLP achieved the best overall performance for BOD prediction (RMSE = 29.8 mg/L, MAPE = 7.0 %), while SVR performed best for NH4+-N prediction (RMSE = 2.3 mg/L, MAPE = 3.3 %). These results demonstrate strong same-day predictive capabilities, enabling more efficient monitoring, conserving laboratory resources, and reducing chemical usage.
Document Type
Journal Article
Date of Publication
10-1-2025
Volume
78
Publication Title
Journal of Water Process Engineering
Publisher
Elsevier
School
Mineral Recovery Research Centre / School of Engineering
Funders
Australian Government Research Training Program Scholarship / Water Corporation of Western Australia
Grant Link
https://doi.org/10.82133/C42F-K220
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Comments
Khoshvaght, H., Permala, R. R., Razmjou, A., & Khiadani, M. (2025). Factors influencing the effectiveness of machine learning algorithms in predicting raw wastewater characteristics: An analysis based on weekly field data. Journal of Water Process Engineering, 78, 108702. https://doi.org/10.1016/j.jwpe.2025.108702