基于注意力机制的时间卷积网络河流总磷预测

黎园园; 刘海隆

doi:10.13205/j.hjgc.202305022

基于注意力机制的时间卷积网络河流总磷预测

doi: 10.13205/j.hjgc.202305022

黎园园,
刘海隆^,

电子科技大学资源与环境学院, 成都 611731

基金项目:

四川省国际合作项目(2020YFH0067)

第三次新疆综合科学考察项目(2021xjkk1404)

详细信息

作者简介:
黎园园(1996-),女,硕士研究生,主要研究方向为生态环境智能监测与信息处理。yuanyuan_li20@163.com

通讯作者:
刘海隆(1974-),男,教授,主要研究方向为生态环境监测。liuhl@uestc.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2022-07-26

PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS

LI Yuanyuan,
LIU Hailong^,

School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

摘要

摘要: 深度学习在水质污染监测中的应用研究成为当前热点。为解决水体污染物浓度在上下游之间的不同关联预测问题,以四川省眉山市彭山区岷江水系的TP预测为研究目标,提出了基于时间模式注意力(TPA)机制的时间卷积网络(TCN)预测模型。首先,利用Pearson相关系数对上下游站点处TP浓度进行时空关系分析;然后,利用TCN网络的扩张因果卷积来提取时序数据之间的依赖关系,利用TPA机制学习不同站点TP浓度之间的复杂关系,从而获取不同时间尺度的站点权重;最后,将该模型应用于河流TP浓度预测。结果表明:多站点输入的TPA-TCN模型的评价指标RMSE、MAE和MAPE较单站点输入的TPA-TCN模型分别降低了36.29%、28.18%和25.26%,相比于融入传统注意力机制的TCN模型,融入TPA机制的TCN模型的这3种评价指标分别降低了10.24%、10.78%和9.94%,说明多站点输入的TPA-TCN模型对河流TP浓度预测具有一定的优势。TPA-TCN模型可有效应用于水质监测中,对水质污染物预测具有重要参考意义。
- 水质预测 /
- 时间卷积网络 /
- 时间模式注意力机制 /
- Pearson相关系数
Abstract: The application of deep learning in water pollution monitoring has become a current hot topic. Taking the total phosphorus prediction of Min River water system in Pengshan District, Meishan City, Sichuan Province as the research object, a temporal convolutional network (TCN) prediction model based on temporal pattern attention (TPA) mechanism was proposed to solve the problem that the concentration of water pollutants was related to different time steps between upstream and downstream. Firstly, the Pearson correlation coefficient was applied to analyze the spatiotemporal relationship of total phosphorus concentration at upstream and downstream sites. Then, the dilated convolution and causal convolution of TCN were used to extract the dependencies of the time series data. And the TPA mechanism was used to learn the complex spatial relationship between the time series of total phosphorus concentration at different stations from TCN, which can obtain the station weights at different time steps in the time series data. Finally, this model was applied to predict the total phosphorus concentration of rivers. The research results showed that the multi-site input TPA-TCN model reduced RMSE by 36.29%, MAE by 28.18% and MAPE by 25.26%, compared with that of the single-site input TPA-TCN model. Compared with TCN model integrated with the traditional attention mechanism, the three evaluation metrics of TCN model integrated with TPA mechanism reduced by 10.24%, 10.78% and 9.94%, indicating that the multi-site input TPA-TCN model has certain advantages in predicting the total phosphorus concentration in rivers. The TPA-TCN model can be effectively applied to water quality monitoring, which has important reference significance for the prediction of water pollutants.
- water quality prediction /
- temporal convolutional network /
- temporal pattern attention mechanism /
- Pearson correlation coefficient

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