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Sodium hazard poses a critical threat to agricultural production globally and regionally which has been previously predicted from ground or surface water. Monitoring rainwater quality in this context is ignored but essential for agricultural water management in central Europe. Our study focused to predict sodium adsorption ratio (SAR) from 1985 to 2021 from ten ionic species of rainwater (pH, EC, Cl-, SO4−2, NO3-, NH4+, Na+, K+, Mg2+, Ca2+) employing four machine learning (random forest (RF), gaussian process regression (GU), random subspace (RSS), and artificial neural network-multilayer perceptron (ANN-MLP)) methods at three stations K-puszta (KP), Farkasfa (FAK), and Nyirjes (NYR) of Hungary, central Europe. Exploratory data analysis was performed using the Mann-Kendall test, Pearson correlation, and principal component analysis (PCA). Rainwater composition revealed the highest percentage of SO4−2 ions i.e., 21 to 31%, followed by 10 to 15% of Na+ ions. Mann-Kendall test revealed a significant (p |
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