融合Prophet与PCA技术的CNN-LSTM模型在水质预测中的应用
Application of CNN-LSTM Model Integrating Prophet and PCA Techniques for Water Quality Prediction
肖克 1张建军 2谭文武 3王理 1宋玲毓 2林海军2
作者信息
- 1. 湖南省计量检测研究院,长沙 410014
- 2. 湖南师范大学工程与设计学院,长沙 410081
- 3. 天翼电子商务有限公司广东分公司,广州 510510
- 折叠
摘要
为了降低传统CNN-LSTM模型进行水质预测时可能会出现的错误发生率,提出了一种基于Prophet模型与PCA的CNN-LSTM水质预测方法.在水质监测数据清洗过程中采用Prophet模型进行异常值处理,使用PCA方法对影响变量进行降维,消除变量关联性,把处理结果作为CNN-LSTM模型输入,对水质总氮指标进行预测.通过实验对基于Prophet模型与PCA的CNN-LSTM水质预测方法进行验证,实验结果表明:该方法相对于CNN-LSTM模型在MAE、RMSE和MSE三种评价指标上都有了较大的提升,其中MSE提升了 13%,RMSE提升了 6.7%,MAE提升了 5.6%.
Abstract
To mitigate potential error rates in traditional CNN-LSTM models for water quality prediction,this study proposes an enhanced CNN-LSTM water quality prediction method incorporating the Prophet model and Principal Component Analysis(PCA).In the data preprocessing phase,the Prophet model is employed for outlier detection and handling of water quality monitoring data.PCA is then utilized to reduce the dimensionality of influencing variables and eliminate variable correlations.The processed results serve as input for the CNN-LSTM model to predict the total nitrogen index of water quality.Experimental validation of the proposed method demonstrates significant improvements over the standard CNN-LSTM model across three evaluation metrics:Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Squared Error(MSE).Specifically,the proposed method achieved a 13%reduction in MSE,a 6.7%decrease in RMSE,and a 5.6%improvement in MAE.These results highlight the effectiveness of integrating Prophet and PCA techniques with CNN-LSTM for enhancing water quality prediction accuracy and reliability.
关键词
计量学/水质监测/主成分分析/CNN/LSTM/水质预测Key words
metrology/water quality monitoring/principal component analysis/CNN/LSTM/water quality prediction引用本文复制引用
基金项目
湖南省自然科学基金(2022JJ90013)
湖南省自然科学基金(2023JJ60157)
湖南省自然科学基金(2022JJ90044)
国家自然科学基金(51775185)
湖南省研究生创新基金(QL20230130)
湖南师范大学校企合作(5312201812)
出版年
2024
NETL
NSTL
万方数据