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

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.