This paper presents a machine learning approach for assessing groundwater arsenic contamination levels in Jharkhand, India. The water is essential for sustaining life, and the presence of heavy metals like arsenic poses a carcinogenic and non-carcinogenic risk. In this study, various machine learning models viz Decision tree, Random Forest, Multilayer Perceptron, and Naive Bayes algorithms were applied to classify the samples as safe or unsafe, considering a provisional guide value of 0.01 mg/l as the benchmark. For classification, different parameters viz DEM, subsoil clay content, subsoil silt content, subsoil sand content, subsoil organic content, type of soil, and LULC were considered. Pearson correlation exhibited a positive and a negative relation between considered parameters and arsenic occurrence. Parameters obtained were considered for the classification of arsenic, and various evaluation criteria, such as accuracy, sensitivity, and specificity, were used to analyze models' performance. Among the models, the Random Forest classifier outperforms other classifier models in terms of performance. Thus, the Random Forest model can be used to approximation people prone to arsenic contamination.
展开▼
