Using machine learning architecture to optimize and model the treatment process for saline water level analysis
machine learning; saline water; water level data analysis; water saline particle; water treatment plants
Water is a vital resource that makes it possible for human life forms to exist. The need for freshwater consumption has significantly increased in recent years. Seawater treatment facilities are less dependable and efficient. Deep learning systems have the potential to increase the efficiency as well as the accuracy of salt particle analysis in saltwater, which will benefit water treatment plant performance. This research proposed a novel method for optimization and modelling of the treatment process for saline water based on water level data analysis using machine learning (ML) techniques. Here, the optimization and modelling are carried out using molecular separation-based reverse osmosis Bayesian optimization. Then the modelled water saline particle analysis has been carried out using back propagation with Kernelized support swarm machine. Experimental analysis is carried out based on water salinity data in terms of accuracy, precision, recall, and specificity, computational cost, and Kappa coefficient. The proposed technique attained an accuracy of 92%, precision of 83%, recall of 78%, specificity of 81%, computational cost of 59%, and Kappa coefficient of 78%. 2023, IWA Publishing. All rights reserved.
Rajput S.P.S.; Webber J.L.; Bostani A.; Mehbodniya A.; Arumugam M.; Nanjundan P.; Wendimagegen A.
https://doi.org/10.2166/wrd.2022.069 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153201481&doi=10.2166%2fwrd.2022.069&partnerID=40&md5=46d67b909d8db382ba4c8085ab1813ec
Rajput S.P.S., Department of Civil Engineering, Maulana Azad National Institute of Technology, MP, Bhopal, India; Webber J.L., Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Doha Area, Kuwait; Bostani A., College of Engineering and Applied Sciences, American University of Kuwait, Salmiya, Kuwait; Mehbodniya A., Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Doha Area, Kuwait; Arumugam M., Center for Transdisciplinary Research, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Chennai, India; Nanjundan P., Department of Data Science, Christ University, Lavasa, Maharashtra, Pune, India; Wendimagegen A., College of Natural and Computational Science, Debre Berhan University, Debre Birha, Ethiopia