  Deep learning has achieved impressive prediction performance in the field of
sequence learning recently. Dissolved oxygen prediction, as a kind of
time-series forecasting, is suitable for this technique. Although many
researchers have developed hybrid models or variant models based on deep
learning techniques, there is no comprehensive and sound comparison among the
deep learning models in this field currently. Plus, most previous studies
focused on one-step forecasting by using a small data set. As the convenient
access to high-frequency data, this paper compares multi-step deep learning
forecasting by using walk-forward validation. Specifically, we test
Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Long
Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Recurrent
Neural Network (BiRNN) based on the real-time data recorded automatically at a
fixed observation point in the Yangtze River from 2012 to 2016. By comparing
the average accumulated statistical metrics of root mean square error (RMSE),
mean absolute error (MAE), and coefficient of determination in each time step,
We find for multi-step time series forecasting, the average performance of each
time step does not decrease linearly. GRU outperforms other models with
significant advantages.
