The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water

Abstract

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 μg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater. HIGHLIGHTS Arsenic contamination in groundwater was assessed using a machine learning approach.; Spatial and geostatistical analysis was performed based on the physicochemical properties of water.; Major hydrochemical facies were responsible for arsenic dissolution in groundwater aquifers of the study area.; Assessment of the models based on accuracy, specificity, and sensitivity.; Piper plot to find the type of groundwater in the study area.;

Keywords

• arsenic
• multilayer perceptron
• naïve bayes
• random forest algorithm
• spatial variability