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走向智能水源监测:以物联网为驱动的LSTM建模,用于多步预测河流盐度动态

Sait Mutlu Karahan1, Wouter Vandenbruwaene2, Janelcy Alferes Castano2

  • 1Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000, Gent, Belgium; Vlaamse Instelling voor Technologisch Onderzoek (VITO), Boeretang 200, Mol, 2400, Belgium.

Journal of environmental management
|October 8, 2025
PubMed
概括

使用长短期记忆 (LSTM) 网络预测水质表明,直接,MIMO和DirMO策略在预测河流系统中的电导率 (盐度) 中表现出色. 递归策略在长期准确性方面存在困难.

关键词:
预测电导率的预测水文超参数调整调整长期短期记忆 长期短期记忆多步预测预测策略多步预测策略.盐化是一种盐化过程.时间序列预测时间序列预测

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