Data-driven water quality prediction for wastewater treatment plants DOI Creative Commons
Haitham Abdulmohsin Afan, Wan Hanna Melini Wan Mohtar, Faidhalrahman Khaleel

et al.

Heliyon, Journal Year: 2024, Volume and Issue: 10(18), P. e36940 - e36940

Published: Aug. 28, 2024

Language: Английский

Analysis and prediction of water quality using deep learning and auto deep learning techniques DOI

D. Venkata Vara Prasad,

Lokeswari Venkataramana, P. Senthil Kumar

et al.

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 821, P. 153311 - 153311

Published: Jan. 19, 2022

Language: Английский

Citations

90
A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance DOI
Boyan Xu, Ching Kwek Pooi,

Kar Ming Tan

et al.

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 54, P. 104041 - 104041

Published: July 19, 2023

Language: Английский

Citations

44
Artificial Neural Network Modeling for the Prediction, Estimation, and Treatment of Diverse Wastewaters: A Comprehensive Review and Future Perspective DOI
Muhammad Ibrahim, Adnan Haider, Jun Wei Lim

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 362, P. 142860 - 142860

Published: July 15, 2024

Language: Английский

Citations

17
A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods DOI
Hang Wan, Rui Xu, Meng Zhang

et al.

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 612, P. 128081 - 128081

Published: June 18, 2022

Language: Английский

Citations

68
Prediction of wastewater treatment plant performance through machine learning techniques DOI Creative Commons
Hani Mahanna, Nora El-Rashidy, Mosbeh R. Kaloop

et al.

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 319, P. 100524 - 100524

Published: June 11, 2024

This study investigates the use of developed machine learning techniques for modeling performance AlHayer, Saudi Arabia, wastewater treatment plant (ALWTP). Three physio-chemical characteristics were measured and predicted, including chemical oxygen demand (COD), biological (BOD), suspended solids (SS), at ALWTP. The pre-evaluation collected data revealed effective capabilities ALWTP removal solids, organic, nutrient pollutants. To estimate ALWATP, four evaluated compared. Logistic regression (LR), random forest (RF), gradient boosting (GB), support vector (SVR) designed. evaluation proposed models showed RF outperformed other estimating COD SS with accuracy 91 % 95 in terms coefficient determination (R2); however, GB was found best, 92 %, detecting BOD ALWATP. indicates ensemble models, GB, can be considered a superiority soft solution plant.

Language: Английский

Citations

10
Artificial intelligence driven advances in wastewater treatment: Evaluating techniques for sustainability and efficacy in global facilities DOI Creative Commons

Dhanyashree Narayanan,

Manish Bhat,

Norottom Paul

et al.

Desalination and Water Treatment, Journal Year: 2024, Volume and Issue: 320, P. 100618 - 100618

Published: July 17, 2024

Language: Английский

Citations

10
Insights into the application of explainable artificial intelligence for biological wastewater treatment plants: Updates and perspectives DOI Creative Commons

Abdul Gaffar Sheik,

Arvind Kumar,

Chandra Sainadh Srungavarapu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 144, P. 110132 - 110132

Published: Jan. 31, 2025

Language: Английский

Citations

1
Assessing Agricultural Reuse Potential of Treated Wastewater: A Hybrid Machine Learning Approach DOI Creative Commons
Daniyal Durmuş Köksal, Yeşim Ahi, Mladen Todorović

et al.

Agronomy, Journal Year: 2025, Volume and Issue: 15(3), P. 703 - 703

Published: March 14, 2025

Estimating the quality of treated wastewater is a complex, nonlinear challenge that traditional statistical methods struggle to address. This study introduces hybrid machine learning approach predict key effluent parameters from an advanced biological treatment plant and assesses reuse potential for irrigation. Three artificial intelligence (AI) models, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Fuzzy Logic-Mamdani (FLM), were applied three years daily inlet outlet water data. Logic was employed usability wastewater, with ANFIS categorizing ANN-based high-performance models (low MSE, 74–99% R2) in fuzzy inference system. The qualitative agricultural irrigation ranged 69% 72% based on best-performing model. It estimated could irrigate approximately 35% 20,000-hectare area. By integrating this research enhances accuracy interpretability predictions, providing reliable framework sustainable resource management. findings support optimization processes highlight AI’s role advancing strategies agriculture, ultimately contributing improved efficiency environmental conservation.

Language: Английский

Citations

1
Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data DOI
Jin-Kyu Kang,

Donmoon Lee,

Kimberly Etombi Muambo

et al.

Water Research, Journal Year: 2023, Volume and Issue: 239, P. 120037 - 120037

Published: May 2, 2023

Language: Английский

Citations

17
An integral and multidimensional review on multi-layer perceptron as an emerging tool in the field of water treatment and desalination processes DOI

Anwar Faizaan Reza,

Randeep Singh,

Rohit Kumar Verma

et al.

Desalination, Journal Year: 2024, Volume and Issue: 586, P. 117849 - 117849

Published: June 15, 2024

Language: Английский

Citations

7