Classification of Arsenic in Groundwater Samples Using Extreme Learning Machine and Crow Search Algorithm for Smart Cities

Abstract

This research aims to classify the arsenic contamination in the groundwater along the banks of river Ganga of Varanasi, India. The groundwater is vital for various purposes, including agriculture and drinking. Groundwater contamination with high levels of arsenic pose a significant health risk. To tackle this problem, the authors build a model for classifying arsenic levels in groundwater samples that incorporates the extreme learning machine (ELM) algorithm and crowd search optimisation (CSO) technique. In the hybrid approach, they initialize the ELM components and randomly assign weights while employing CSO to guide the search for optimal solutions. By classifying new groundwater samples as having high or low arsenic concentrations, the developed model can be used to evaluate the new groundwater samples. The proposed hybrid approach offers a promising solution for monitoring and managing groundwater quality, ensuring a healthier environment for the city's population.