Chen Zhan-Feng, Li Xiao-Fang
Guangdong Ecological and Environmental Monitoring Center, Guangzhou 510308, China.
Huan Jing Ke Xue. 2024 Jun 8;45(6):3205-3213. doi: 10.13227/j.hjkx.202306024.
To improve the accuracy and stability of water quality prediction in the Pearl River Estuary, a water quality prediction model was proposed based on BiLSTM improved with an attention mechanism. The feature attention mechanism was introduced to enhance the ability of the model to capture important features, and the temporal attention mechanism was added to improve the mining ability of time series correlation information and water quality fluctuation details. The new model was applied to the water quality prediction of eight estuaries of the Pearl River, and the prediction performance test, generalization ability test, and characteristic parameter expansion test were carried out. The results showed that:① The new model achieved high prediction accuracy in the water quality prediction of the Zhuhaidaqiao section. The root-mean-square error (RMSE) between the predicted value and the measured value was 0.004 1 mg·L, and the coefficient of determination () was 98.3 %. Compared with that of Multi-BiLSTM, Multi-LSTM, BiLSTM, and LSTM, the results showed that the new model had the highest prediction accuracy, which verified the accuracy of the model. ② Both the number of training samples and the number of forecasting steps affected the prediction accuracy of the model, and the prediction accuracy of the model increased with the increase of the training samples. When predicting the total phosphorus of the Zhuhaidaqiao section, more than 240 training samples could obtain higher prediction accuracy. Increasing the number of prediction steps caused the prediction accuracy of the model to decline rapidly, and the reliability of the model prediction could not be guaranteed when the number of prediction steps was greater than 5. ③ When the new model was applied to the prediction of different water quality indexes in eight estuaries of the Pearl River, the prediction results had high precision and the model had strong generalization ability. The input data of upstream water quality, rainfall, and other characteristic parameters associated with the section prediction index of the object could improve the prediction accuracy of the model. Through many tests, the results showed that the new model could meet the requirements of precision, applicability, and expansibility of water quality prediction in the Pearl River Estuary and thus is a new exploration method for high-precision prediction of water quality in complex hydrodynamic environments.
为提高珠江口水质预测的准确性和稳定性，提出了一种基于注意力机制改进的双向长短期记忆网络（BiLSTM）的水质预测模型。引入特征注意力机制以增强模型捕捉重要特征的能力，并添加时间注意力机制以提高时间序列相关信息和水质波动细节的挖掘能力。将新模型应用于珠江八大河口的水质预测，并进行了预测性能测试、泛化能力测试和特征参数扩展测试。结果表明：①新模型在珠海大桥断面水质预测中取得了较高的预测精度。预测值与实测值之间的均方根误差（RMSE）为0.004 1 mg·L，决定系数（）为98.3%。与多双向长短期记忆网络（Multi - BiLSTM）、多长短期记忆网络（Multi - LSTM）、双向长短期记忆网络（BiLSTM）和长短期记忆网络（LSTM）相比，结果表明新模型具有最高的预测精度，验证了模型的准确性。②训练样本数量和预测步长均影响模型的预测精度，且模型的预测精度随训练样本数量的增加而提高。在预测珠海大桥断面总磷时，超过240个训练样本可获得较高的预测精度。增加预测步长会导致模型的预测精度迅速下降，当预测步长大于5时，无法保证模型预测的可靠性。③当将新模型应用于珠江八大河口不同水质指标的预测时，预测结果具有较高的精度，模型具有较强的泛化能力。与目标断面预测指标相关的上游水质、降雨等特征参数的输入数据可提高模型的预测精度。通过多次测试，结果表明新模型能够满足珠江口水质预测的精度、适用性和扩展性要求，是复杂水动力环境下高精度水质预测的一种新探索方法。
