Mapping Redox Conditions and pH in Indian Drinking Water System Using Machine Learning Models

Abstract

The successful utility of groundwater resources requires estimation of contaminants occurring at a regional scale and for the risk estimation of drinking water supplies. Based on the patterns and predictions of the ground water age and temperature conditions, areas and volumes of the locations throughout India have been verified, and they have opted for the proportion making of the drinking water resources. The data from 300 to 500 drinking water wells have been collected from the government dataset belonging to 18 states in India. The pH and oxygen levels of the drinking water have been considered for the project and are used for the classification of data and this classified data is being used for the mapping of locations over the Indian map. Thus, the results are to be visualized at the end of the project. Accuracy modulations have been done based on the machine learning models used for the execution.