# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 6 Conclusions  
This paper proposes a hybrid prediction model to solve the challenge of poor DO concentration forecasting accuracy ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019) in aquaculture management. The hybrid model was designed by combining the EEMD technique with LSTM NN. The applied EEMD method allowed for the decomposition of the original sensor data into multiple IMFs. Furthermore, this method is used to carefully select IMFs that are strongly correlated with the original sensor data through a feature selection process, and integrate both into inputs for the neural network. The actual experimental WQ data from a fish farm show that the hybrid model provides good results and outperforms related models with high accuracy, as indicated by error metrics shown in Table 2. For future work, a hybrid EEMD-based multi-variate prediction model can be explored to propose a more comprehensive water quality forecasting and analysis. Additionally, more WQ measuring sites will also be considered to expand this model. **Author Contributions:** Conceptualization, E.E. and T.A. ; methodology, E.E. ; software, E.E. ; validation, E.E. and T.A. ; formal analysis, E.E. ; investigation, E.E. ; resources, E.E. and T.A. ; data curation, E.E. ; writing--original draft preparation, E.E. ; writing--review and editing, T.A. ; visualization, E.E. ; supervision, T.A. ; project administration, T.A. ; funding acquisition, T.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Innovate UK/BBSRC (ref: 86204028, BB/S020896/1). **Acknowledgments:** The authors wish to thank their project partners, the researchers at Chinese Agricultural University, for providing the raw data used in this study. The authors also wish to thank the reviewers for their comments and suggestions, which have greatly helped in improving the quality of the paper. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Error Statistics** & **12 Hour Forecast** & **1 Month Forecast** \\ \hline MAE & 0.0753 & 0.1666 \\ MSE & 0.0065 & 0.0385 \\ RMSE & 0.0807 & 0.1962 \\ MAPE & 0.0093 & 0.0206 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error Statistics for Short-term and Long-term DO Content Forecasting. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Error Statistics** & **LSTM NN** & **BPNN** & **SAE-LSTM NN** & **SAE-BPNN** & **EEMD-LSTM NN** \\ \hline Run Time(s) & 23.2 & 3.6 & 29.6 & 9.1 & **2.37** \\ MAE & 0.1590 & 0.4530 & 0.1260 & 0.4060 & **0.0753** \\ MSE & 0.0398 & 0.3013 & 0.0242 & 0.2428 & **0.0065** \\ RMSE & 0.1995 & 0.5489 & 0.1556 & 0.4927 & **0.0807** \\ MAPE & 0.0160 & 0.0450 & 0.0130 & 0.0419 & **0.0093** \\ \hline \hline \end{tabular}  
* Back-propagation neural network (BPNN) model, radial basis function neural network (RBFNN) model, long short-term memory (LSTM) model, sparse auto-encoder (SAE) and LSTM (SAE-LSTM) NN model, and SAE-BPNN model [PERSON] et al. (2019), and our proposed ensemble EMD based LSTM (EEMD-LSTM) NN model.  
\end{table} 
Table 2: Performance Comparison with Related Forecasting Models *. * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network. _Sustainability_**2020**, _12_, 3678. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Mode decomposition based deep learning model for multi-section traffic prediction. _World Wide Web_**2020**, _23_, 2513-2527. [CrossRef]
* [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Wind power prediction based on variational mode decomposition multi-frequency combinations. _J. Mod. Power Syst. Clean Energy_**2019**, \(7\), 281-288. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] A novel combined forecasting model for short-term wind power based on ensemble empirical mode decomposition and optimal virtual prediction. _J. Renew. Sustain. Energy_**2016**, \(8\), 1-22.