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

Deep learning in water protection of resources, environment, and ecology: achievement and challenges DOI
Xiaohua Fu, Jie Jiang,

Xie Wu

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(10), P. 14503 - 14536

Published: Feb. 2, 2024

Language: Английский

Citations

6
Forecasting influent wastewater quality by chaos coupled machine learning optimized with Bayesian algorithm DOI

D. H. S. Ramkumar,

V. Jothiprakash

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 61, P. 105306 - 105306

Published: April 16, 2024

Language: Английский

Citations

6
Novel production prediction model of gasoline production processes for energy saving and economic increasing based on AM-GRU integrating the UMAP algorithm DOI
Jintao Liu, Liangchao Chen, Wei Xu

et al.

Energy, Journal Year: 2022, Volume and Issue: 262, P. 125536 - 125536

Published: Sept. 22, 2022

Language: Английский

Citations

28
Investigating Machine Learning Applications for Effective Real-Time Water Quality Parameter Monitoring in Full-Scale Wastewater Treatment Plants DOI Open Access
Usman Safder,

Jongrack Kim,

Gijung Pak

et al.

Water, Journal Year: 2022, Volume and Issue: 14(19), P. 3147 - 3147

Published: Oct. 6, 2022

Environmental sensors are utilized to collect real-time data that can be viewed and interpreted using a visual format supported by server. Machine learning (ML) methods, on the other hand, excellent in statistically evaluating complicated nonlinear systems assist modeling prediction. Moreover, it is important implement precise online monitoring of complex wastewater treatment plants increase stability. Thus, this study, novel approach based ML methods suggested predict effluent concentration total nitrogen (TNeff) few hours ahead. The method consists different algorithms training stage, best selected models concatenated prediction stage. Recursive feature elimination reduce overfitting curse dimensionality finding eliminating irrelevant features identifying optimal subset features. Performance indicators multi-attention-based recurrent neural network partial least squares had highest accurate performance, representing 41% improvement over methods. Then, proposed was assessed with multistep horizons. It predicted 1-h ahead TNeff 98.1% accuracy rate, whereas 3-h TN 96.3% rate.

Language: Английский

Citations

27
An artificial neural network-based data filling approach for smart operation of digital wastewater treatment plants DOI
Yu Shen, Huimin Li, Bing Zhang

et al.

Environmental Research, Journal Year: 2023, Volume and Issue: 224, P. 115549 - 115549

Published: Feb. 22, 2023

Language: Английский

Citations

14
Comparing artificial and deep neural network models for prediction of coagulant amount and settled water turbidity: Lessons learned from big data in water treatment operations DOI
Subin Lin, Jiwoong Kim, Chuanbo Hua

et al.

Journal of Water Process Engineering, Journal Year: 2023, Volume and Issue: 54, P. 103949 - 103949

Published: June 30, 2023

Language: Английский

Citations

14
Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China DOI

Jing Chen,

H. Li,

Manirankunda Felix

et al.

Environmental Science and Pollution Research, Journal Year: 2024, Volume and Issue: 31(10), P. 14610 - 14640

Published: Jan. 26, 2024

Language: Английский

Citations

5
Tackling data challenges in forecasting effluent characteristics of wastewater treatment plants DOI
Ali Mohammad Roohi, Sara Nazif, Pouria Ramazi

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 354, P. 120324 - 120324

Published: Feb. 15, 2024

Language: Английский

Citations

5
Prediction Model of Ammonia Nitrogen Concentration in Aquaculture Based on Improved AdaBoost and LSTM DOI Creative Commons
Yiyang Wang,

Dehao Xu,

Xianpeng Li

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(5), P. 627 - 627

Published: Feb. 20, 2024

The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the too high, it will seriously affect survival state aquaculture. Therefore, prediction control in advance essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) Long Short-Term Memory neural network (LSTM) to predict mariculture. Firstly, weight assignment strategy was improved, number correction iterations introduced retard shortcomings data error accumulation caused by AdaBoost basic algorithm. Then, XAdaBoost algorithm generated several LSTM su-models concentration. Finally, there were two experiments conducted verify effectiveness model. In experiment, compared with other comparison models, RMSE XAdaBoost–LSTM reduced about 0.89–2.82%, MAE 0.72–2.47%, MAPE 8.69–18.39%. stability RMSE, MAE, decreased 1–1.5%, 0.7–1.7%, 7–14%. From these experiments, evaluation indexes superior which proves that has good accuracy lays foundation monitoring regulating change future.

Language: Английский

Citations

5
Machine learning framework for wastewater circular economy — Towards smarter nutrient recoveries DOI Creative Commons
Allan Soo, Li Gao, Ho Kyong Shon

et al.

Desalination, Journal Year: 2024, Volume and Issue: 592, P. 118092 - 118092

Published: Sept. 7, 2024

Language: Английский

Citations

5