Considering the great importance of groundwater quality for water supply, in the last decade, significant scientific attention has been devoted to nitrate reduction transformation pathways and nitrogen conservation in groundwaters in the form of ammonium. To evaluate and assess the ability of machine learning models to predict the ammonium concentration, four machine learning models were applied: a three-layer neural network (NN), a deep neural network (DNN), and two variants of support vector regression (SVR) models: with linear and with Gaussian radial basis function kernel. A dataset of 322 samples with 13 predictor variables representing selected parameters responsible for oxidative/reductive nitrogen transformations in shallow alluvial groundwater was acquired from measurements in 55 monitoring wells during a 6-year monitoring period (2011–2016) in Serbia. Applied principal component analysis and cluster analysis gave an insight into conditionality and relations between the selected parameters, distinguishing four main factors, which explained 70.97% of total variance, and classifying examined objects by similarity. Extracted factors correlated the concentration patterns, implying the main nitrogen transformations in examined groundwater. The machine learning models were successfully applied for predicting the ammonium concentration with high determination coefficients (R²) in tests: 0.84 for DNN and 0.64 for NN, while the SVR did not prove to be adequate with the best R² of 0.24.

考虑到地下水质量对供水的重要性，在过去的十年中，大量的科学注意力集中在硝酸盐还原转化途径和以铵形式存在的地下水氮的养护中。为了评估和评估机器学习模型预测铵浓度的能力，应用了四种机器学习模型：三层神经网络（NN），深层神经网络（DNN）和支持向量回归（SVR）的两种变体）模型：具有线性和高斯径向基函数核。在塞尔维亚的一个为期6年的监测期内（2011-2016年），从55口监测井的测量数据中，获取了322个样本的数据集，其中13个预测变量代表负责浅冲积地下水中氧化/还原氮转化的所选参数。应用主成分分析和聚类分析可深入了解所选参数之间的条件和关系，区分出四个主要因素，这些因素可解释总方差的70.97％，并按相似性对检查对象进行分类。提取的因子与浓度模式相关，这意味着被检查的地下水中主要的氮转化。机器学习模型已成功应用于具有高确定系数的氨浓度预测（应用主成分分析和聚类分析可深入了解所选参数之间的条件和关系，区分四个主要因素，这些因素可解释总方差的70.97％，并按相似性对检查对象进行分类。提取的因子与浓度模式相关，这意味着被检查的地下水中主要的氮转化。机器学习模型已成功应用于具有高确定系数的氨浓度预测（应用主成分分析和聚类分析可以深入了解所选参数之间的条件和关系，区分出四个主要因素，这些因素可以解释总方差的70.97％，并按相似度对检查对象进行分类。提取的因子与浓度模式相关，这意味着被检查的地下水中主要的氮转化。机器学习模型已成功应用于具有高确定系数的氨浓度预测（暗示了所检查的地下水中的主要氮转化。机器学习模型已成功应用于具有高确定系数的氨浓度预测（暗示了所检查的地下水中的主要氮转化。机器学习模型已成功应用于具有高确定系数的氨浓度预测（测试中的R ²）：DNN为0.84，NN为0.64，而SVR不足以证明最佳R ²为0.24。