Detection and Prediction of HMS from Drinking Water by Analysing the Adsorbents from Residuals Using Deep Learning

Abstract

Contamination HM is an important issue associated with the environment, and it requires suitable steps for the reduction of HMs in water at an acceptable ratio. With modern technologies, this could be possible by enabling the carbon adsorbents to adsorb the pollutions via deep learning strategies. In this paper, we develop a model on detection and prediction of presence of HMs from drinking water by analysing the adsorbents from residuals using deep learning. The study uses dense neural networks or DenseNets to analyse the microscopic images of the residual adsorbents. The study initially preprocesses and extracts features using standardised procedure. The DenseNets are used finally for detection purpose, and it is trained and tested with standard set of microscopic images. The experimental results are conducted to test the efficacy of the deep learning model on detecting the HM composition. The results of simulation show that the proposed deep learning model achieves 95% higher rate of detecting the HM composition from the adsorption residuals than other methods.