基于小波去噪和LSTM的Seq2Seq水质预测模型  

Seq2Seq Water Quality Prediction Model Based on Wavelet Denoising and LSTM

作　　者：袁梅雪 魏守科 孙铭 赵金东 YUAN Mei-Xue;WEI Shou-Ke;SUN Ming;ZHAO Jin-Dong(School of Computer and Control Engineering,Yantai University,Yantai 264005,China;Deepsim Intelligent Information Technology Co.Ltd.,Beijing 100089,China)

机构地区：[1]烟台大学计算机与控制工程学院,烟台264005 [2]北京迪普迅智能信息技术有限公司,北京100089

出　　处：《计算机系统应用》2022年第6期38-47,共10页Computer Systems & Applications

基　　金：山东省自然科学基金(ZR2020MF148)。

摘　　要：建立水质模型预测水质变化是保障饮用水安全、人类健康和维持生态平衡的关键.本文提出了基于小波分解去噪和LSTM的双层双向Seq2Seq混合模型(W-Bi2Seq2Seq)来预测水质的变化.使用Daubechies5 (db5)小波将数据集分解为低频序列和高频序列,高频序列作为噪声去除,仅保留低频信号用作所提出模型的输入.选取了烟台市门楼水库的4项水质指标数据(pH、氨氮、电导率和浊度)用于模型的训练,验证和测试.所提出的小波双层双向模型(Bi2)与小波单层单向模型(Uni1)、小波单层双向模型(Bi1)、小波双层单向模型(Uni2)、传统的LSTM模型以及基于小波分解的LSTM模型(W-LSTM),进行比较实验.其实验结果显示,在训练过程中, 4个Seq2Seq模型都具有很好的性能,都能够很好拟合4项水质指标的历史数据集.然而,测试结果表明, Bi2在预测精度和泛化能力方面优于其他5个模型,并且显著提高复杂度较高的水质数据的预测精度.Building water quality models to predict variations in water quality is essential for drinking water safety,human health, and ecological balance. In this study, a bidirectional two-layer hybrid Seq2Seq model based on wavelet decomposition denoising and long short-term memory(LSTM), i.e., a W-Bi2Seq2Seq model, is proposed to predict changes in water quality. The Daubechies5(db5) wavelet is used to decompose datasets into low-frequency series and high-frequency ones. The high-frequency series are removed as noise, and only the low-frequency signal is kept and used as the input to the proposed model. Data series of four water quality indices(pH, NH3-N, conductivity, and turbidity) are collected from the Menlou Reservoir in Yantai, Shandong Province for model training, verification, and testing. The proposed wavelet-based bidirectional two-layer model(Bi2) is compared with the wavelet-based unidirectional one-layer model(Uni1), wavelet-based bidirectional one-layer model(Bi1), wavelet-based unidirectional two-layer model(Uni2),traditional LSTM model, and LSTM model based on wavelet decomposition(W-LSTM). The experimental results show that all the four Seq2Seq models have favorable performance in fitting the historical datasets of the four indices during the training process. Nevertheless, the testing results indicate that Bi2 is superior to the other five models in terms of prediction accuracy and generalization ability and significantly improves the prediction accuracy on water quality data with high complexity.

关 键 词：水质预测 小波去噪 Daubechies5 LSTM Seq2Seq模型 小波分析 深度学习 门楼水库 

分 类 号：X52[环境科学与工程—环境工程] TP183[自动化与计算机技术—控制理论与控制工程]