基于VMD-TCN-GRU模型的水质预测研究
Water Quality Prediction Based on VMD-TCN-GRU Model
项新建 1许宏辉 1谢建立 2丁祎 1胡海斌 1郑永平 1杨斌2
作者信息
- 1. 浙江科技学院 自动化与电气工程学院,浙江 杭州 310023
- 2. 凯铭科技(杭州)有限公司,浙江 杭州 310023
- 折叠
摘要
为充分挖掘水质数据在短时震荡中的变化特征,提升预测模型的精度,提出一种基于VMD(变分模态分解)、TCN(卷积时间神经网络)及GRU(门控循环单元)组成的混合水质预测模型,采用VMD-TCN-GRU模型对汾河水库出水口高锰酸盐指数进行预测,并与此类研究中常见的SVR(支持向量回归)、LSTM(长短期记忆神经网络)、TCN和CNN-LSTM(卷积神经网络-长短期记忆神经网络)这 4 种模型预测结果对比表明:VMD-TCN-GRU模型能更好挖掘水质数据在短时震荡过程中的特征信息,提升水质预测精度;VMD-TCN-GRU模型的MAE(平均绝对误差)、RMSE(均方根误差)下降,R2(确定系数)提高,其MAE、RMSE、R2 分别为0.055 3、0.071 7、0.935 1;其预测性能优越,预测精度更高且拥有更强的泛化能力,可以应用于汾河水质预测.
Abstract
In order to fully excavate the variation characteristics of water quality data in short-term shocks and improve the accuracy of pre-diction model,we proposed a VMD-TCN-GRU water quality prediction model based on Variational Mode Decomposition(VMD),Temporal Convolutional Network(TCN),and Gated Recurrent Unit(GRU).The VMD-TCN-GRU model was applied to predict the permanganate in-dex at the outlet of Fenhe River Reservoir.Comparative analysis with commonly used models in water quality prediction,including Support Vector Regression(SVR),Long Short-Term Memory(LSTM),TCN,and Convolutional Neural Network and Long Short-Term Memary(CNN-LSTM),demonstrates that the VMD-TCN-GRU model excels in extracting features during short-term oscillations in water quality data,identifying actual patterns of variation,facilitating comprehensive model learning,and thereby improving prediction accuracy.The VMD-TCN-GRU model achieves a Mean Absolute Error(MAE)of 0.055 3,Root Mean Square Error(RMSE)of 0.071 7,and a determination co-efficient R2 of 0.935 1,indicating high predictive accuracy and strong generalization capabilities.This model can be effectively applied to wa-ter quality prediction tasks in the Fenhe River.
关键词
水质预测/混合模型/变分模态分解/卷积时间神经网络/门控循环单元/时间序列/汾河Key words
water quality prediction/mixed model/Variational Mode Decomposition/Temporal Convolution Network/Gated Recurrent Unit/time series/Fenhe River引用本文复制引用
基金项目
浙江省自然科学基金资助项目(LY19F030004)
浙江省自然科学基金资助项目(LQ16F030002)
浙江省重点研发计划项目(202206)
杭州市科技发展计划项目(202203B21)
出版年
2024
国家科技期刊平台
NSTL
万方数据