The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R²) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

中文翻译：

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基于1-DRCNN和BiGRU混合神经网络模型的the河水质预测

当前的全球水环境已受到严重破坏。水质参数的预测可以为将来的水质状况和水质改善提供有效的参考资料。为了进一步提高水质预测的准确性以及模型的稳定性和泛化能力，我们提出了一种新的综合深度学习水质预测算法。首先，通过隔离林，拉格朗日插值法，滑动窗平均值和主成分分析（PCA）对水质数据进行清洗和预处理。然后，使用一维残差卷积神经网络（1-DRCNN）和双向门控递归单元（BiGRU）提取水质参数中的潜在局部特征，并整合时间序列前后的信息。最后，使用一个完整的连接层来获得总氮（TN），总磷（TP）和高锰酸钾指数（COD-Mn）的最终预测结果。我们的预测实验是根据2018年7月5日至2019年3月26日在河北省唐山市the河三个控制段的大黑亭水库，Lu县桥和江葛庄的实际水质数据进行的。该方法的绝对百分比误差（MAPE）为2.4866，测定系数（R²）能够达到0.9431。实验结果表明，与现有的LSTM，GRU和BiGRU模型相比，本文提出的模型具有更高的预测精度和泛化能力。