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

• Published in: Water science and technology Vol. 88; no. 3; pp. 595 - 614
• Main Authors: Chattopadhyay, Arghya, Singh, Anand Prakash, Kumar, Siddharth, Pati, Jayadeep, Rakshit, Amitava
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 01.08.2023
• Subjects: Accuracy; Algorithms; Analytical chemistry; Anthropogenic factors; Aquifers; Arsenic; Carcinogens; Contamination; Data collection; Decision trees; Geostatistics; Groundwater; Groundwater pollution; Learning algorithms; Machine learning; Mathematical models; multilayer perceptron; Multilayer perceptrons; naïve bayes; Physicochemical processes; Physicochemical properties; random forest algorithm; Regression analysis; Sensitivity; Spatial analysis; spatial variability; Specificity; Statistical analysis; Water analysis; Water pollution; Water sampling; Wind; Bangladesh; India

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 th...