Prediction of Water Quality Index in Drinking Water Distribution System Using Activation Functions Based Ann

Determination of the drastic changes in water quality is an urgent need in this polluted era and is more essential for the survival of the existing and growing water demand. It has been very difficult to analyze the water quality data. This study focused on the Water Quality Index (WQI) prediction of water samples collected from 1944 different wells surrounding the Vellore district. WQI prediction is carried out by ANN (i.e.) Artificial Neural Networks implementation which has used 15 groundwater variables that are collected in different parts of the Vellore district from 2008 to 2017. If 15 underground variable values meet the desired range then WQI is considered as better and appropriate for drinking. But if any one of the value doesn’t meet the desired range then it is not considered as better and hence not suitable for drinking. In this study the pre-processing of the collected data has been completed to reduce the computational time. Further feature extraction techniques are used to extract the required features. The extracted features are passed on to ANN classifiers that possess three activation functions like Tanh, Maxout, and rectifier. The novelty of this paper is that WQI is determined by combining the three activation functions like Tanh, Maxout, and rectifier. A comparative analysis has been performed for proposed work related with various methodologies.