Department of Computer Science and Engineering, Northwest Normal University, Lanzhou, Gansu Province, China.
Gansu Academy of Eco-Environmental Sciences, Lanzhou, Gansu Province, China.
Environ Sci Pollut Res Int. 2022 Oct;29(50):75664-75680. doi: 10.1007/s11356-022-21115-y. Epub 2022 Jun 3.
Accurate prediction of water quality contributes to the intelligent management and control of watershed ecology. Water Quality data has time series characteristics, but the existing models only focus on the forward time series when LSTM is introduced and do not consider the effect of the reverse time series on the model. Also did not take into account the different contributions of water quality sequences to the model at different moments. In order to solve this problem, this paper proposes a watershed water quality prediction model called AT-BILSTM. The model mainly contains a Bi-LSTM layer and a temporal attention layer and introduces an attention mechanism after bidirectional feature extraction of water quality time series data to highlight the data series that have a critical impact on the prediction results. The effectiveness of the method was verified with actual datasets from four monitoring stations in Lanzhou section of the Yellow River basin in China. After comparing with the reference model, the results show that the proposed model combines the bidirectional nonlinear mapping capability of Bi-LSTM and the feature weighting feature of the attention mechanism. Taking Fuhe Bridge as an example, compared with the original LSTM model, the RMSE and MAE of the model are reduced to 0.101 and 0.059, respectively, and the R2 is improved to 0.970, which has the best prediction performance among the four cross-sections and can provide a decision basis for the comprehensive water quality management and pollutant control in the basin.
准确的水质预测有助于流域生态的智能管理和控制。水质数据具有时间序列特征，但现有模型在引入 LSTM 时仅关注前向时间序列，而不考虑反向时间序列对模型的影响。也没有考虑到水质序列在不同时刻对模型的不同贡献。为了解决这个问题，本文提出了一种名为 AT-BILSTM 的流域水质预测模型。该模型主要包含一个 Bi-LSTM 层和一个时间注意力层，并在水质时间序列数据的双向特征提取后引入注意力机制，突出对预测结果有重大影响的数据序列。该方法通过中国黄河兰州段四个监测站的实际数据集进行了验证。与参考模型相比，结果表明，所提出的模型结合了 Bi-LSTM 的双向非线性映射能力和注意力机制的特征加权特征。以福和桥为例，与原始 LSTM 模型相比，模型的 RMSE 和 MAE 分别降低到 0.101 和 0.059，R2 提高到 0.970，在四个断面中的预测性能最好，可以为流域综合水质管理和污染物控制提供决策依据。
