Abstract：
Ammonia nitrogen (AN) pollution frequently occurs in urban rivers with the continuous acceleration of industrialization. Monitoring AN pollution levels and tracing its complex sources often require large-scale testing, which are time-consuming and costly. Due to the lack of reliable data samples, there were few studies investigating the feasibility of water quality prediction of AN concentration with a high fluctuation and non-stationary change through data-driven models. In this study, four deep-learning models based on neural network algorithms including artificial neural network (ANN), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) were employed to predict AN concentration through some easily monitored indicators such as pH, dissolved oxygen, and conductivity, in a real AN-polluted river. The results showed that the GRU model achieved optimal prediction performance with a mean absolute error (MAE) of 0.349 and coefficient of determination (R2) of 0.792. Furthermore, it was found that data preprocessing by the VMD technique improved the prediction accuracy of the GRU model, resulting in an R2 value of 0.822. The prediction model effectively detected and warned against abnormal AN pollution (> 2 mg/L), with a Recall rate of 93.6% and Precision rate of 72.4%. This data-driven method enables reliable monitoring of AN concentration with high-frequency fluctuations and has potential applications for urban river pollution management.
摘要：
随着工业化进程的不断加快,城市河流氨氮污染频繁发生。监测AN污染水平和追踪其复杂来源通常需要大规模测试，这既耗时又昂贵。由于缺乏可靠的数据样本，很少有研究通过数据驱动模型对具有高波动和非平稳变化的AN浓度进行水质预测的可行性。在这项研究中,基于神经网络算法的四种深度学习模型，包括人工神经网络(ANN)，递归神经网络(RNN)，长短期记忆(LSTM)，和门控复发单位(GRU)被用来通过一些容易监测的指标来预测AN浓度，如pH，溶解氧,和导电性，在一个真正的污染河流。结果表明，GRU模型实现了最佳预测性能，平均绝对误差（MAE）为0.349，确定系数（R2）为0.792。此外，结果发现，通过VMD技术进行数据预处理提高了GRU模型的预测精度，导致0.822的R2值。该预测模型有效地检测并警告了AN污染异常(>2mg/L)，召回率为93.6%，准确率为72.4%。这种数据驱动的方法能够可靠地监测具有高频波动的AN浓度，并且在城市河流污染管理中具有潜在的应用。
