基于DA-CNN-BiLSTM的河流溶解氧浓度预测  

River Dissolved Oxygen Prediction Based on DA⁃CNN⁃BiLSTM

作　　者：谢小良 吴琳琳 XIE Xiaoiang;WU Linin(College of Science,Hunan University of Technology and Business,Changsha 410000,China)

机构地区：[1]湖南工商大学理学院,湖南长沙410000

出　　处：《人民黄河》2024年第7期92-97,111,共7页Yellow River

基　　金：湖南省自然科学基金资助项目(2022JJ30213)。

摘　　要：溶解氧是衡量水质的重要指标,对溶解氧浓度的准确预测,可以为水环境管理和水污染防治工作提供科学依据。考虑溶解氧受外界多种复杂因素影响,数据具有强烈的非线性和非平稳性特征,提出了DA-CNN-BiLSTM溶解氧浓度预测模型,其中CNN层用于提取数据局部特征,空间注意力机制关注对预测结果具有更高影响的特征,BiLSTM挖掘输入序列的前向和后向邻域信息,时间注意力机制捕捉不同时刻的时间依赖性。将模型应用于福建闽江3个水质监测站的溶解氧浓度预测中,通过与基线模型的对比表明:相较于基线模型,DA-CNN-BiLSTM模型对DO浓度具有更好的预测效果,模型的预测值更接近于实测值,溶解氧浓度预测性能最优;加入空间注意力机制后,模型的预测性能得到提升。Dissolved oxygen is an important index to measure water quality.The accurate prediction of dissolved oxygen can provide reference for scientific management and protection of water environment.Considering that dissolved oxygen was affected by many external factors and the data had strong nonlinear and non-stationary characteristics,the DA-CNN-BiLSTM dissolved oxygen prediction model was proposed.The CNN layer was used to extract local features of data.Spatial attention focused on features that had a higher impact on prediction outcomes.BiLSTM mined the attribute relationships of the input sequence.Temporal attention layer captured the time dependence of different moments.The model was applied to the dissolved oxygen prediction of three water quality monitoring stations in Minjiang River,Fujian Province.By comparing with the baseline model,it shows that the DA-CNN-BiLSTM model has a better prediction of DO concentration compared with the baseline model.The predicted value of the model is closer to the measured value and the DO prediction performance is optimal.The predic-tion performance of the model has been improved after adding the spatial attention mechanism.

关 键 词：注意力机制 CNN-BiLSTM模型 时间序列预测 溶解氧浓度预测 

分 类 号：X32[环境科学与工程—环境工程]