A Hybrid ConvLSTM Deep Neural Network for Noise Reduction and Data Augmentation for Prediction of Non-linear Dynamics of Streamflow

J. Rochac, N. Zhang, T. Deksissa, Jiajun Xu, Lara A. Thompson
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Abstract

Long Short-Term Memory (LSTM) models are at the cutting edge of artificial learning and ecoinformatics in regards to water quantity prediction. However, one driver for more accuracy, efficient, and robust, water pollution perdition methods is climate change, and in particular global sea level rising. Statistical systems are no longer reliable and new prediction models need to be explored due to the increasing nonlinearity of streamflow predictors and extremes sea level changes. Another driver is that, in places with legacy infrastructure, updated water monitoring systems and unreliable forecasting framework, state-of-the-art LSTM -based models suffer due to the presence of noisy data. This paper proposes multiple LSTM-based models with Scharr filtering to improve the streamflow prediction accuracy against noise. A hybrid ConvLSTM approach is realized to overcome the nonlinearity of the main predictors and the noises. The evaluation results demonstrate that the proposed hybrid ConvLSTM model can effectively improve the overall prediction accuracy for both real-world data and the noise-augmented data. The hybrid ConvLSTM model also obtained competitive and even better performance compared with several state-of-the-art methods. In addition, our proposed design achieves comparable performance in terms of prediction time.
基于混合ConvLSTM深度神经网络的流场非线性动力学降噪与数据增强预测
长短期记忆(LSTM)模型在水量预测方面处于人工学习和生态信息学的前沿。然而,气候变化,特别是全球海平面上升,是更准确、更有效、更可靠的水污染预测方法的一个驱动因素。由于流量预测器的非线性增加和海平面的极端变化,统计系统不再可靠,需要探索新的预测模式。另一个驱动因素是,在基础设施陈旧、水监测系统更新和预测框架不可靠的地方,最先进的基于LSTM的模型由于噪声数据的存在而受到影响。本文提出了多个基于lstm的沙尔滤波模型,以提高对噪声的流量预测精度。为了克服主要预测量和噪声的非线性,实现了一种混合卷积stm方法。评价结果表明,所提出的混合ConvLSTM模型可以有效提高实际数据和噪声增强数据的整体预测精度。混合ConvLSTM模型也获得了具有竞争力甚至更好的性能。此外,我们提出的设计在预测时间方面达到了相当的性能。
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