摘要
随着地表水水质恶化日益严重,有效的水质预警预测技术对于水资源的可持续发展与应急响应机制实施至关重要。长短期记忆网络在水质时间序列预测问题中被广泛使用,但是仅使用长短期记忆网络进行水质预测并不能解决各种复杂因素造成的水质序列的不规则波动问题。为了解决该问题,提出一种数据驱动的水质预测混合模型,该模型将基于局部加权回归散点平滑(Loess)的季节与趋势分解(STL)算法与基于编解码的长短期记忆网络(LSTM-ED)结合。首先通过STL的加法模型将水质时间序列分解为3个子序列,然后利用多元LSTM-ED神经网络对子序列进行预测,通过叠加将数据恢复为实际值,最后通过季节性分段的拉依达准则进一步判断水质是否存在异常并做出预警。实验结果表明,与单一的LSTM、LSTM-ED以及基于序列分解的LSTM-ED模型相比,所提出的模型能显著地提高水质时间序列预测的精度和可靠性,并为水质动态预警提供有效的数据支持。
Surface water quality is increasingly deteriorated in recent years,and therefore,high-quality early warning and prediction of water quality are essential for sustainability of water resources and emergency response mechanisms.Long short-term memory(LSTM)network is widely applied in the existing literature on the prediction of water quality time series.However,only applying LSTM for the prediction of water quality time series cannot well address irregular fluctuations in the water quality series caused by multiple complex factors.To solve this problem,a data-driven prediction model for the water quality time series was proposed,named STL-LSTM-ED,which was composed of seasonal-trend decomposition using locally weighted scatterplot smoothing(STL)and LSTM based on encoder-decoder(LSTM-ED).Compared with several typical models of LSTM,LSTM-ED,and a sequence decomposition method based on LSTM,the proposed STL-LSTM-ED can significantly improve the prediction accuracy and reliability of the water quality time series,and also provide the effective data support for dynamic warning of water quality.
作者
许博文
毕敬
苑海涛
王功明
乔俊飞
XU Bowen;BI Jing;YUAN Haitao;WANG Gongming;QIAO Junfei(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China)
出处
《智能科学与技术学报》
2021年第4期456-465,共10页
Chinese Journal of Intelligent Science and Technology
基金
水体污染控制与治理科技重大专项(No.2018ZX07111005)
国家自然科学基金资助项目(No.61802015,No.62073005,No.62173013)。
关键词
季节性分解
长短期记忆网络
水质动态预警
异常检测
seasonal decomposition
long short-term memory network
dynamicwarning of water quality
anomaly detection
