# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 5 Discussions  
Our study used an hourly centered moving average value for the DO concentration time series data. In this study, EEMD, which is a powerful technique for signal decomposition, was used for decomposing the non-linear and non-stationary signal. Decomposing the DO concentration  
Figure 3: Four months’ distribution of the DO concentration raw data. time series sensor data is an integral part of the proposed hybrid EEMD-based LSTM forecasting model, for forecasting the short-term values of the DO concentration. The adopted EEMD technique decomposed the original DO concentration sensor time series data into eight (8) relatively stable IMFs (IMF 1 to IMF 8) and one residual item (see Figure 4). The EEMD amplitude of Gaussian white noise was set to 0.2. Finally, all the extracted sub-band signals by the adopted EEMD technique were utilized in the decomposition step of the proposed hybrid EEMD-based LSTM forecasting model. The EEMD trend was extracted through the summation of low-frequency IMFs. Figure 5 depicts the non-stationary continuous signal of the real DO concentration composed of sinusoidal waves with a distinct change in frequency. In Figure 3 is shown the distribution of the collected DO concentration raw dataset; it can be observed that at a certain period, the level of DO concentration dropped to its lowest level, by up to 4.0 mg/L and 3.9 mg/L. The proposed hybrid model was applied in this area and the result is presented in Figure 6. Figure 6 shows the area with DO concentration downward decrease to a meagre 4.0 mg/L up to 3.9 mg/L before picking up again by increasing towards DO concentration of up to 5.25 mg/L. The graphs in Figures 7 and 8 reveal that the new hybrid model provided good results and successfully forecasted DO concentration with a high-level of accuracy for both Short-term forecast and Long-term forecast. It is also noteworthy to mention that Figure 8 illustrates fluctuations in the range of 7.0 mg/L to 8.5 mg/L, which are not critical for aquaculture, since aquatic life can only suffer harm, with a possibility of death, when such DO concentration decreases. Figure 9 shows error graphics of the proposed hybrid EEMD-based LSTM forecasting model performance: short- and long-term forecast. Figure 4: Decomposition by EEMD illustrating 8 out of 8 intrinsic mode functions (IMFs). In Table 1, the error statistics for both short-term and long-term DO content forecasting performance of our proposed hybrid model are shown. The steep increase in error gap between the short-term and long-term DO content forecasting performance of our proposed hybrid model indicates that the nearer the forecast future the higher the forecast accuracy, and vice versa. Figure 9 shows the forecasting error statistics, using bar charts for both short-term and song-term DO concentration to further emphasize the increase in prediction error as the forecasting period increases. Figure 5: Non-stationary continuous signal composed of sinusoidal waves with a distinct change in frequency. Figure 6: Short-term (6 h) forecast result measured against actual dissolved oxygen (DO) concentration values. Figure 8: Long-term forecast result measured against actual DO concentration values. Figure 7: Short-term (12 h) forecast result measured against actual DO concentration values. Figure 9: Forecasting error statistics for short-term and long-term DO concentration. In Table 2, the performance of the proposed hybrid EEMD-based LSTM NN is compared with another related hybrid water quality prediction model, based on sparse auto-encoder (SAE) and LSTM NN, SAE-BPNN, single LSTM and BPNN developed by [PERSON] et al. [PERSON] et al. (2019). The tabulated error statistics indicate that our proposed hybrid model outperforms the other models as listed in Table 2, in terms of the error margin of the forecasted data. This performance gain over the other related prediction models is because our proposed hybrid model applied the EEMD method to effectively decompose the original signal into its constituent several intrinsic sub-sequences.