English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep learning has achieved impressive prediction performance in the field ofsequence learning recently. Dissolved oxygen prediction, as a kind oftime-series forecasting, is suitable for this technique. Although manyresearchers have developed hybrid models or variant models based on deeplearning techniques, there is no comprehensive and sound comparison among thedeep learning models in this field currently. Plus, most previous studiesfocused on one-step forecasting by using a small data set. As the convenientaccess to high-frequency data, this paper compares multi-step deep learningforecasting by using walk-forward validation. Specifically, we testConvolutional Neural Network (CNN), Temporal Convolutional Network (TCN), LongShort-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional RecurrentNeural Network (BiRNN) based on the real-time data recorded automatically at afixed observation point in the Yangtze River from 2012 to 2016. By comparingthe 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 eachtime step does not decrease linearly. GRU outperforms other models withsignificant advantages.

Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction