https://www.selleckchem.com/products/ap20187.html
Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XG, were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern
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