Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm
Abstract
:1. Introduction
2. Methods and Materials
2.1. Study Area Description and Datasets Analysis
2.2. Data Filling and Correction
2.3. Data Correlation Analysis
3. Prediction Model Design
3.1. EEMD Method
- (1)
- The total number of extrema and the zero-crossings of IMFs must be equal or, at most, differ by one.
- (2)
- The mean of local minima and local maxima envelopes is zero at any point.
- (1)
- Ride waves identification and eradication, and
- (2)
- IMFs’ wave profiles refining to obtain more symmetric wave profiles.
- (a)
- Determine all the extrema of the signal .
- (b)
- Apply the linear interpolation technique between minima (respectively, maxima) with envelopes, .
- (c)
- Compute the mean of the envelopes , with representing the number of iterations.
- (d)
- Then, extract the detail .
- (e)
- Repeat step (a) to step (d) until the IMFs converged with satisfy the definition of the IMFs.
- (f)
- Repeat step (a) to step (e) to determine the residual , with .
3.2. DL-LSTM Neural Networks
- (a)
- Forget gate equation:
- (b)
- Input gate equations:
- (c)
- Output gate equations:
- (d)
- Cell state equation:
3.3. Proposed Unique Hybrid EEMD-DL-LSTM NN-Based Water Quality Parameters Prediction Model
4. Performance Evaluation Metrics
5. Results and Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Dissolved Oxygen | Turbidity | pH | Temperature | |
---|---|---|---|---|
Dissolved Oxygen | 1 | −0.09677 | 0.54826 | −0.14893 |
Turbidity | −0.09677 | 1 | −0.03914 | −0.05654 |
pH | 0.54826 | −0.03914 | 1 | 0.55366 |
Temperature | −0.14893 | −0.05654 | 0.55366 | 1 |
MAE | RMSE | MAPE | |||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
pH | Temperature | DO | Turbidity | pH | Temperature | DO | Turbidity | pH | Temperature | DO | Turbidity | ||||||||||||
DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM | DL-LSTM | EEMD-DL-LSTM |
0.0042 | 0.0140 | 0.0421 | 0.0251 | NA | 0.0262 | NA | 0.0309 | 0.6236 | 0.0407 | 0.0519 | 0.0325 | NA | 0.0355 | NA | 0.0291 | 0.0092 | 0.0074 | 0.0850 | 0.0073 | NA | 0.0075 | NA | 0.0078 |
Statistical Evaluation Metrics | BP Model | SAE-BP Model | DL-LSTM Model | SAE-LSTM Model | EEMD-DL-LSTM Model |
---|---|---|---|---|---|
Run Time (s) | 3.4000 | 9.1000 | 22.0000 | 28.2000 | 2.3700 |
MAE | 0.3000 | 0.2580 | 0.1000 | 0.0690 | 0.0375 |
MSE | 0.1352 | 0.0953 | 0.0166 | 0.0077 | 0.0024 |
RMSE | 0.3680 | 0.3090 | 0.1290 | 0.0880 | 0.0489 |
MAPE | 0.0310 | 0.0270 | 0.0100 | 0.0070 | 0.0072 |
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Eze, E.; Halse, S.; Ajmal, T. Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm. Water 2021, 13, 1782. https://doi.org/10.3390/w13131782
Eze E, Halse S, Ajmal T. Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm. Water. 2021; 13(13):1782. https://doi.org/10.3390/w13131782
Chicago/Turabian StyleEze, Elias, Sarah Halse, and Tahmina Ajmal. 2021. "Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm" Water 13, no. 13: 1782. https://doi.org/10.3390/w13131782
APA StyleEze, E., Halse, S., & Ajmal, T. (2021). Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm. Water, 13(13), 1782. https://doi.org/10.3390/w13131782