Machine Learning Methods for the Prediction of Wastewater Treatment Efficiency and Anomaly Classification with Lack of Historical Data
Source abstract
Abstract
This study examines an algorithm for collecting and analyzing data from wastewater treatment facilities, aimed at addressing regression tasks for predicting the quality of treated wastewater and classification tasks for preventing emergency situations, specifically filamentous bulking of activated sludge. The feasibility of using data obtained under laboratory conditions and simulating the technological process as a training dataset is explored. A small dataset collected from actual wastewater treatment plants is considered as the test dataset. For both regression and classification tasks, the best results were achieved using gradient-boosting models from the CatBoost family, yielding metrics of SMAPE = 9.1 and ROC-AUC = 1.0. A set of the most important predictors for modeling was selected for each of the target features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Hydrological Forecasting Using AI · Water Quality Monitoring Technologies · Water Systems and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research and Education Centre “Water Supply and Wastewater Treatment”, Moscow State University of Civil Engineering, 26, Yaroslaskoye Highway, Moscow 129337, Russia
Department of Information Systems, Technologies and Automation in Construction, Moscow State University of Civil Engineering, 26, Yaroslaskoye Highway, Moscow 129337, Russia
Funding
National Research Moscow State University of Civil Engineering
References (37)
Show source-provided references
1. Machine learning modeling for the prediction of phosphorus and nitrogen removal efficiency and screening of crucial microorganisms in wastewater treatment plants10.1016/j.scitotenv.2023.167730 ↗
2. A machine learning framework to improve effluent quality control in wastewater treatment plants10.1016/j.scitotenv.2021.147138 ↗
3. A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance10.1016/j.jwpe.2023.104041 ↗
4. Estimating the chemical oxygen demand of petrochemical wastewater treatment plants using linear and nonlinear statistical models—A case study10.1016/j.chemosphere.2020.129465 ↗
5. Recio-Colmenares, R., León Becerril, E., Gurubel Tun, K.J., and Conchas, R.F. (2023). Design of a Soft Sensor Based on Long Short-Term Memory Artificial Neural Network (LSTM) for Wastewater Treatment Plants. Sensors, 23.10.3390/s23229236 ↗
6. Forecasting effluent and performance of wastewater treatment plant using different machine learning techniques10.1016/j.jwpe.2021.102380 ↗
7. Singh, N.K., Yadav, M., Singh, V., Padhiyar, H., Kumar, V., Bhatia, S.K., and Show, P.L. (2023). Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems. Bioresour. Technol., 369.10.1016/j.biortech.2022.128486 ↗
8. Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm10.1007/s11356-023-27224-6 ↗
9. An evolutionary robust soft measurement technique via enhanced atom search optimization and outlier robust extreme learning machine for wastewater treatment process10.1016/j.jwpe.2023.104102 ↗
10. Estimating discharge coefficient of side weirs in trapezoidal and rectangular flumes using outlier robust extreme learning machine10.1007/s13201-022-01698-0 ↗
11. Outlier robust extreme learning machine to simulate discharge coefficient of side slots10.1007/s13201-022-01687-3 ↗
12. Application of machine learning techniques to model a full-scale wastewater treatment plant with biological nutrient removal10.1016/j.jece.2022.107430 ↗
13. Hybrid model composed of machine learning and ASM3 predicts performance of industrial wastewater treatment10.1016/j.jwpe.2024.105888 ↗
14. Alali, Y., Harrou, F., and Sun, Y. (2023). Unlocking the potential of wastewater treatment: Machine learning based energy consumption prediction. Water, 15.10.3390/w15132349 ↗
15. Energy requirements for water production, treatment, end use, reclamation, and disposal10.1016/j.rser.2012.05.022 ↗
16. Wastewater treatment aeration process optimization: A data mining approach10.1016/j.jenvman.2016.07.047 ↗
17. Simulation of the biochemical and chemical oxygen demand and total suspended solids in wastewater treatment plants: Data-mining approach10.1016/j.jclepro.2021.126533 ↗
18. Safder, U., Kim, J., Pak, G., Rhee, G., and You, K. (2022). Investigating machine learning applications for effective real-time water quality parameter monitoring in full-scale wastewater treatment plants. Water, 14.10.3390/w14193147 ↗
19. Free nitrous acid prediction in ANAMMOX process using hybrid deep neural network model10.1016/j.jenvman.2023.118566 ↗
20. Artificial intelligence-implemented prediction and cost-effective optimization of micropollutant photodegradation using g-C3N4/Bi2O3 heterojunction10.1016/j.cej.2024.156029 ↗
21. Water quality prediction model using Gaussian process regression based on deep learning for carbon neutrality in papermaking wastewater treatment system10.1016/j.envres.2022.112942 ↗
22. Machine learning methods for anomaly classification in wastewater treatment plants10.1016/j.jenvman.2023.118594 ↗
23. Machine learning classification algorithms for inadequate wastewater treatment risk mitigation10.1016/j.psep.2022.01.065 ↗
24. Water quality classification using machine learning algorithms10.1016/j.jwpe.2022.102920 ↗
25. Optimization of a Novel Engineered Ecosystem Integrating Carbon, Nitrogen, Phosphorus, and Sulfur Biotransformation for Saline Wastewater Treatment Using an Interpretable Machine Learning Approach10.1021/acs.est.4c03160 ↗
26. Al Nuaimi, H., Abdelmagid, M., Bouabid, A., Chrysikopoulos, C.V., and Maalouf, M. (2023). Classification of WatSan Technologies using machine learning techniques. Water, 15.10.3390/w15152829 ↗
27. Spatially adaptive machine learning models for predicting water quality in Hong Kong10.1016/j.jhydrol.2023.129649 ↗
28. Halalsheh, N., Alshboul, O., Shehadeh, A., Al Mamlook, R.E., Al-Othman, A., Tawalbeh, M., Almuflih, A.S., and Papelis, C. (2022). Breakthrough curves prediction of selenite adsorption on chemically modified zeolite using boosted decision tree algorithms for water treatment applications. Water, 14.10.3390/w14162519 ↗
29. Prediction of wastewater treatment quality using LSTM neural network10.1016/j.eti.2021.101632 ↗
30. Burrichter, B., Koltermann da Silva, J., Niemann, A., and Quirmbach, M. (2024). A Temporal Fusion Transformer Model to Forecast Overflow from Sewer Manholes during Pluvial Flash Flood Events. Hydrology, 11.10.3390/hydrology11030041 ↗
31. Sun, X., Zhang, L., Wang, C., Yang, Y., and Wang, H. (2024). Dynamic Real-Time Prediction of Reclaimed Water Volumes Using the Improved Transformer Model and Decomposition Integration Technology. Sustainability, 16.10.3390/su16156598 ↗
32. Prediction and Detection of Sewage Treatment Process Using N-BEATS Autoencoder Network10.1109/ACCESS.2022.3216924 ↗
33. Hao, Z. (2024). A dissolved oxygen prediction model based on GRU–N-Beats. Front. Mar. Sci., 11.10.3389/fmars.2024.1365047 ↗
34. Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning10.1109/TPDS.2019.2953745 ↗
35. Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling10.1016/j.psep.2021.12.034 ↗
36. Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods10.1016/j.jenvman.2021.113941 ↗
37. Gogina, E., and Gulshin, I. (2021). Characteristics of low-oxygen oxidation ditch with improved nitrogen removal. Water, 13.10.3390/w13243603 ↗