Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence DOI
Fuad Bin Nasir, Jin Li

Water Environment Research, Journal Year: 2024, Volume and Issue: 96(10)

Published: Sept. 25, 2024

Abstract This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques understanding impact variables behind prediction. Three ML models, random forest (RF), gradient boosting (GBM), tree (GBT), were evaluated their performance using statistical indicators. Input variable combinations selected through different feature selection (FS) methods. XAI employed to enhance interpretability transparency models. The results suggest that prediction accuracy depends on choice model number variables. found be effective in interpreting decisions made by each model. provides an example production applying understand factors influencing it. Understandable interpretation can facilitate targeted interventions process optimization improve efficiency sustainability processes. Practitioner Points Explainable play a crucial role promoting trust between real‐world applications. Widely practiced used predict United States plant. Feature methods reduce required input without compromising accuracy. explain driving

Language: Английский

Optimizing Membrane Bioreactor Performance in Wastewater Treatment Using Machine Learning and Meta-Heuristic Techniques DOI Creative Commons
Usman M. Ismail, Khalid Bani‐Melhem, Muhammad Faizan Khan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104626 - 104626

Published: March 1, 2025

Language: Английский

Citations

6
The pivotal and transformative role of artificial intelligence in advanced multidimensional modeling and optimization of complex cefixime separation processes using 3-hydroxyphenol-formaldehyde nanostructures: A multi-layered analytical approach DOI
Hossein Azarpira,

Parsa Khakzad,

Mohammad Reza Alipour

et al.

Microchemical Journal, Journal Year: 2025, Volume and Issue: 213, P. 113817 - 113817

Published: April 29, 2025

Language: Английский

Citations

1
Understanding machine learning predictions of wastewater treatment plant sludge with explainable artificial intelligence DOI
Fuad Bin Nasir, Jin Li

Water Environment Research, Journal Year: 2024, Volume and Issue: 96(10)

Published: Sept. 25, 2024

Abstract This study investigates the use of machine learning (ML) models for wastewater treatment plant (WWTP) sludge predictions and explainable artificial intelligence (XAI) techniques understanding impact variables behind prediction. Three ML models, random forest (RF), gradient boosting (GBM), tree (GBT), were evaluated their performance using statistical indicators. Input variable combinations selected through different feature selection (FS) methods. XAI employed to enhance interpretability transparency models. The results suggest that prediction accuracy depends on choice model number variables. found be effective in interpreting decisions made by each model. provides an example production applying understand factors influencing it. Understandable interpretation can facilitate targeted interventions process optimization improve efficiency sustainability processes. Practitioner Points Explainable play a crucial role promoting trust between real‐world applications. Widely practiced used predict United States plant. Feature methods reduce required input without compromising accuracy. explain driving

Language: Английский

Citations

2