38 citations as recorded by crossref.

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2. Constructing a visual detection method for coagulation effect based on image feature machine learning S. Li et al. 10.1016/j.jwpe.2024.106354
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4. Evaluation of water treatment plant using Artificial Neural Network (ANN) case study of Pimpri Chinchwad Municipal Corporation (PCMC) D. Wadkar et al. 10.1007/s40899-021-00532-w
5. Removal of brilliant green dye from synthetic wastewater under batch mode using chemically activated date pit carbon R. Mansour et al. 10.1039/D0RA08488C
6. Artificial Neural Network (ANN) Modelling of Palm Oil Mill Effluent (POME) Treatment with Natural Bio-coagulants N. Mohd Najib et al. 10.1007/s40710-020-00431-w
7. Data-driven modeling techniques for prediction of settled water turbidity in drinking water treatment S. McKelvey et al. 10.3389/fenve.2024.1401180
8. An Intelligent Dosing Algorithm Model for Wastewater Treatment Plant X. Fang et al. 10.1088/1742-6596/2224/1/012027
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10. Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) Analysis Modelling for Predicting Chemical Dosages of a Water Treatment Plant (WTP) of Drinking Water S. Gyparakis et al. 10.3390/w17020227
11. Towards sustainable and energy efficient municipal wastewater treatment by up-concentration of organics H. Guven et al. 10.1016/j.pecs.2018.10.002
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13. Enhancing Coagulation Prediction in Water Treatment Using a Similarity Score–Based Piecewise Machine Learning Model J. Zhang et al. 10.1061/JOEEDU.EEENG-7969
14. Emerging evolutionary algorithm integrated with kernel principal component analysis for modeling the performance of a water treatment plant S. Abba et al. 10.1016/j.jwpe.2019.101081
15. Constructing a visual detection model for floc settling velocity using machine learning S. Li et al. 10.1016/j.jenvman.2024.122805
16. Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization M. Achite et al. 10.1007/s10668-022-02523-z
17. Modelling coagulant dosage in drinking water treatment plant using advance machine learning model: Hybrid extreme learning machine optimized by Bat algorithm H. Boumezbeur et al. 10.1007/s11356-023-27224-6
18. Development of reservoir’s optimum operation rules considering water quality issues and climatic change data analysis B. Yaghoubi et al. 10.1016/j.scs.2020.102467
19. A combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a drinking water treatment plant A. Chiavola et al. 10.1016/j.jprocont.2023.103068
20. A precise dosing system based on a coagulation reaction model for water treatment C. Huang et al. 10.1016/j.jwpe.2025.106962
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22. Characteristic analysis of floc size distribution and image texture evolution in chemical coagulation process S. Li et al. 10.1016/j.psep.2025.107298
23. Application of soft computing in water treatment plant and water distribution network D. Wadkar et al. 10.1080/23249676.2021.1978881
24. A look-ahead Monte Carlo simulation method for improving parental selection in trait introgression S. Moeinizade et al. 10.1038/s41598-021-83634-x
25. Application of Artificial Intelligence in the Management of Coagulation Treatment Engineering System J. Liu et al. 10.3390/pr12091824
26. Application of a 2k–p Fractional Experimental Design in Coagulation-Flocculation Processes in the Treatment of Wastewater from a Slaughterhouse J. Carpintero et al. 10.3390/su141610402
27. An efficient neural network model for aiding the coagulation process of water treatment plants C. Jayaweera & N. Aziz 10.1007/s10668-021-01483-0
28. Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model S. Lin et al. 10.1016/j.watres.2023.119665
29. Cationic Starch and Polyaluminum Chloride as Coagulants for River Nile Water Treatment S. Abdo et al. 10.1016/j.gsd.2020.100331
30. Complementarity‐based selection strategy for genomic selection S. Moeinizade et al. 10.1002/csc2.20070
31. Exploring potential dual-stage attention based recurrent neural network machine learning application for dosage prediction in intelligent municipal management X. Fang et al. 10.1039/D2EW00560C
32. Assessment of poly(diallyl dimethyl ammonium chloride) and lime for surface water treatment (pond, river, and canal water): seasonal variations and correlation analyses S. Jabin et al. 10.1007/s10661-024-13004-3
33. Data to intelligence: The role of data-driven models in wastewater treatment M. Bahramian et al. 10.1016/j.eswa.2022.119453
34. Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities S. Chowdhury & T. Karanfil 10.1016/j.chemosphere.2024.141958
35. Potential application of natural coagulant extraction from walnut seeds for water turbidity removal T. Zedan et al. 10.2166/wpt.2022.019
36. Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants A. Librantz & F. dos Santos 10.3390/su15086579
37. Determination of coagulant dosages for process control using online UV-vis spectra of raw water Z. Shi et al. 10.1016/j.jwpe.2021.102526
38. Establishment of Relationship Between Coagulant and Chlorine Dose Using Artificial Neural Network D. Wadkar et al. 10.1007/s40996-024-01546-y