Using machine learning architecture to optimize and model the treatment process for saline water level analysis

Abstract

Abstract 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%.