Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Site and Data Collection
2.2. Selection of the Predictor Variables
2.3. Model Development and Parameter Selection
3. Results and Discussion
3.1. Model Performance Comparison and Validation
3.2. Model Test: Comparing Continuous Forecasting with Real Data
3.3. An Application of the Model as an Alarm System
- Case 1:
- Observed salinity ≥ 0.25 , and forecasted salinity ≥ 0.25 is true positive (TP);
- Case 2:
- Observed salinity ≥ 0.25 , and forecasted salinity < 0.25 is false negative (FN);
- Case 3:
- Observed salinity < 0.25 , and forecasted salinity < 0.25 is true negative (TN);
- Case 4:
- Observed salinity < 0.25 , and forecasted salinity ≥ 0.25 is false positive (FP).
4. Conclusions
- The residual analysis of the the multiple linear regression (MLR) indicated that the model behaviours were different at high and low water levels, therefore, a two-stage model (the combined model) was proposed, analysed and compared to the single model;
- Two model fitting methods, which were the MLR and the artificial neuron network (ANN), were investigated;
- The combined model using MLR and ANN performed better than single models in forecasting hourly salinity at 24 to 120 h;
- When the forecast period increased from 24 to 120 h, the forecasting performances of all the models decreased rapidly. Still, the combined model performed better than the single model in all cases;
- The forecasting model can be effectively utilized as an alarm system. The confusion matrix shows the performance of the alarm system for early warning of a saltwater intrusion event at various forecast periods. The accuracy decreases as the forecast periods increases. In practice, a suitable forecast period can be selected based on the user’s needs.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
MWA | The Metropolitan Waterworks Authority |
HII | Hydro Informatics Institute |
RTN | Royal Thai Navy |
MLR | Multiple Linear Regression |
ANN | Artificial Neural Network |
FP | Forecast Period |
LWL | Low Water Level |
HWL | High Water Level |
AWL | All Water Level |
Appendix A. The Residual Analysis and the Model Validation
Appendix A.1. The Residual Analysis and the Single Model Validation
Forecast Period (h) | Candidate 1A | Candidate 1B | Candidate 1C | ||||||
---|---|---|---|---|---|---|---|---|---|
RMSE | MAPE | RMSE | MAPE | RMSE | MAPE | ||||
24 | 0.783 | 0.049 | 6.260 | 0.800 | 0.053 | 6.981 | 0.806 | 0.050 | 6.699 |
48 | 0.673 | 0.061 | 9.638 | 0.642 | 0.070 | 10.777 | 0.609 | 0.069 | 10.305 |
72 | 0.583 | 0.066 | 11.993 | 0.529 | 0.078 | 13.492 | 0.518 | 0.079 | 12.753 |
96 | 0.471 | 0.073 | 14.076 | 0.443 | 0.087 | 15.880 | 0.429 | 0.084 | 15.101 |
120 | 0.371 | 0.081 | 15.204 | 0.389 | 0.091 | 17.597 | 0.363 | 0.090 | 16.810 |
Forecast Period (h) | Candidate 1A | Candidate 1B | Candidate 1C | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE | MAPE | Structure | RMSE | MAPE | Structure | RMSE | MAPE | Structure | ||||
24 | 0.800 | 0.047 | 5.939 | 5-6-1 | 0.812 | 0.051 | 6.353 | 4-2-1 | 0.814 | 0.049 | 6.087 | 2-9-1 |
48 | 0.686 | 0.060 | 8.894 | 5-9-1 | 0.655 | 0.069 | 9.531 | 4-5-1 | 0.636 | 0.067 | 8.482 | 2-3-1 |
72 | 0.595 | 0.066 | 10.729 | 5-17-1 | 0.544 | 0.077 | 11.980 | 4-5-1 | 0.545 | 0.077 | 10.221 | 2-4-1 |
96 | 0.488 | 0.072 | 12.493 | 5-8-1 | 0.456 | 0.086 | 14.608 | 4-18-1 | 0.457 | 0.082 | 11.942 | 2-10-1 |
120 | 0.391 | 0.080 | 13.912 | 5-5-1 | 0.405 | 0.090 | 15.845 | 4-17-1 | 0.409 | 0.087 | 12.889 | 2-4-1 |
Appendix A.2. The Residual Analysis and the Combined Model Validation
Forecast Period (h) | Candidate 2A | Candidate 2B | ||||
---|---|---|---|---|---|---|
RMSE | MAPE | RMSE | MAPE | |||
24 | 0.720 | 0.063 | 7.459 | 0.731 | 0.063 | 8.053 |
48 | 0.557 | 0.083 | 11.249 | 0.565 | 0.082 | 12.815 |
72 | 0.518 | 0.077 | 13.899 | 0.476 | 0.093 | 15.705 |
96 | 0.364 | 0.091 | 16.039 | 0.364 | 0.103 | 18.263 |
120 | 0.310 | 0.092 | 17.981 | 0.300 | 0.106 | 19.803 |
Forecast Period (h) | Candidate 2C | Candidate 2D | ||||
RMSE | MAPE | RMSE | MAPE | |||
24 | 0.731 | 0.063 | 8.082 | 0.729 | 0.064 | 7.813 |
48 | 0.564 | 0.082 | 12.895 | 0.557 | 0.083 | 12.244 |
72 | 0.474 | 0.093 | 15.816 | 0.461 | 0.094 | 14.788 |
96 | 0.360 | 0.103 | 18.408 | 0.339 | 0.105 | 17.137 |
120 | 0.295 | 0.107 | 19.974 | 0.263 | 0.109 | 18.777 |
Forecast Period (h) | Candidate 2A | Candidate 2B | ||||||
---|---|---|---|---|---|---|---|---|
RMSE | MAPE | Structure | RMSE | MAPE | Structure | |||
24 | 0.731 | 0.061 | 6.895 | 4-16-1 | 0.753 | 0.060 | 7.162 | 3-4-1 |
48 | 0.575 | 0.081 | 10.553 | 4-18-1 | 0.593 | 0.079 | 11.659 | 3-3-1 |
72 | 0.526 | 0.077 | 12.622 | 4-3-1 | 0.486 | 0.092 | 14.241 | 3-6-1 |
96 | 0.380 | 0.089 | 14.834 | 4-8-1 | 0.376 | 0.103 | 16.532 | 3-18-1 |
120 | 0.325 | 0.092 | 16.762 | 4-10-1 | 0.316 | 0.105 | 18.710 | 3-9-1 |
Forecast Period (h) | Candidate 2C | Candidate 2D | ||||||
RMSE | MAPE | Structure | RMSE | MAPE | Structure | |||
24 | 0.754 | 0.060 | 7.364 | 2-3-1 | 0.752 | 0.060 | 6.486 | 1-3-1 |
48 | 0.593 | 0.080 | 11.421 | 2-3-1 | 0.588 | 0.080 | 9.887 | 1-4-1 |
72 | 0.483 | 0.093 | 14.256 | 2-8-1 | 0.475 | 0.093 | 12.878 | 1-3-1 |
96 | 0.381 | 0.102 | 16.815 | 2-5-1 | 0.353 | 0.104 | 13.900 | 1-14-1 |
120 | 0.307 | 0.106 | 17.989 | 2-14-1 | 0.288 | 0.107 | 15.692 | 1-6-1 |
Forecast Period (h) | Candidate 3A | Candidate 3B | ||||
---|---|---|---|---|---|---|
RMSE | MAPE | RMSE | MAPE | |||
24 | 0.950 | 0.007 | 2.656 | 0.950 | 0.007 | 2.647 |
48 | 0.884 | 0.011 | 4.413 | 0.884 | 0.011 | 4.398 |
72 | 0.878 | 0.010 | 5.280 | 0.877 | 0.010 | 5.286 |
96 | 0.869 | 0.010 | 5.932 | 0.869 | 0.011 | 5.957 |
120 | 0.845 | 0.011 | 6.386 | 0.844 | 0.012 | 6.432 |
Forecast Period (h) | Candidate 3C | Candidate 3D | ||||
RMSE | MAPE | RMSE | MAPE | |||
24 | 0.935 | 0.010 | 2.897 | 0.935 | 0.010 | 2.937 |
48 | 0.858 | 0.013 | 5.318 | 0.859 | 0.013 | 5.304 |
72 | 0.809 | 0.016 | 6.211 | 0.807 | 0.016 | 6.108 |
96 | 0.816 | 0.015 | 7.147 | 0.802 | 0.016 | 7.695 |
120 | 0.753 | 0.018 | 7.980 | 0.729 | 0.019 | 9.016 |
Forecast Period (h) | Candidate 3A | Candidate 3B | ||||||
---|---|---|---|---|---|---|---|---|
RMSE | MAPE | Structure | RMSE | MAPE | Structure | |||
24 | 0.946 | 0.008 | 2.650 | 4-5-1 | 0.948 | 0.007 | 2.713 | 3-12-1 |
48 | 0.879 | 0.011 | 4.489 | 4-3-1 | 0.884 | 0.011 | 4.277 | 3-3-1 |
72 | 0.881 | 0.010 | 5.156 | 4-3-1 | 0.880 | 0.010 | 5.181 | 3-3-1 |
96 | 0.872 | 0.010 | 5.809 | 4-2-1 | 0.871 | 0.010 | 5.796 | 3-7-1 |
120 | 0.849 | 0.011 | 6.274 | 4-6-1 | 0.848 | 0.011 | 6.254 | 3-4-1 |
Forecast Period (h) | Candidate 3C | Candidate 3D | ||||||
RMSE | MAPE | Structure | RMSE | MAPE | Structure | |||
24 | 0.946 | 0.008 | 3.156 | 2-4-1 | 0.944 | 0.009 | 3.139 | 1-17-1 |
48 | 0.863 | 0.013 | 5.262 | 2-2-1 | 0.852 | 0.013 | 5.231 | 1-4-1 |
72 | 0.805 | 0.016 | 6.162 | 2-5-1 | 0.797 | 0.016 | 6.309 | 1-4-1 |
96 | 0.812 | 0.015 | 6.857 | 2-2-1 | 0.794 | 0.016 | 7.618 | 1-11-1 |
120 | 0.772 | 0.018 | 7.292 | 2-2-1 | 0.726 | 0.020 | 8.927 | 1-17-1 |
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Model | Case | Candidate | |
---|---|---|---|
MLR | ANN | ||
Single model | AWL | 1A: Equation (1) | 1A: Equation (1) |
Combined model | LWL | 2A: Equation (4) | 2A: Equation (4) |
HWL | 3A: Equation (8) | 3B: Equation (9) |
Forecast Period (h) | MLR | ANN | ||||||
---|---|---|---|---|---|---|---|---|
RMSE | MAPE | NSE | RMSE | MAPE | NSE | |||
24 | 0.860 | 0.073 | 10.487 | 0.776 | 0.865 | 0.069 | 10.327 | 0.796 |
48 | 0.741 | 0.099 | 14.975 | 0.585 | 0.735 | 0.096 | 12.150 | 0.605 |
72 | 0.530 | 0.115 | 18.886 | 0.416 | 0.576 | 0.110 | 16.255 | 0.469 |
96 | 0.412 | 0.120 | 19.731 | 0.299 | 0.460 | 0.119 | 16.100 | 0.319 |
120 | 0.365 | 0.113 | 21.556 | 0.272 | 0.448 | 0.111 | 18.280 | 0.290 |
Forecast Period (h) | MLR | ANN | ||||||
---|---|---|---|---|---|---|---|---|
RMSE | MAPE | NSE | RMSE | MAPE | NSE | |||
24 | 0.888 | 0.058 | 8.131 | 0.853 | 0.892 | 0.054 | 7.671 | 0.875 |
48 | 0.734 | 0.091 | 13.177 | 0.643 | 0.748 | 0.085 | 11.168 | 0.687 |
72 | 0.583 | 0.108 | 14.922 | 0.480 | 0.599 | 0.104 | 13.624 | 0.514 |
96 | 0.449 | 0.120 | 16.249 | 0.338 | 0.493 | 0.111 | 15.531 | 0.434 |
120 | 0.392 | 0.117 | 16.797 | 0.283 | 0.448 | 0.107 | 16.172 | 0.395 |
Forcast Period (h) | MLR | ANN |
---|---|---|
24 | 9.03% | 9.03% |
48 | 9.02% | 11.94% |
72 | 13.33% | 8.75% |
96 | 11.54% | 26.50% |
120 | 3.89% | 26.58% |
Forecast Period (h) | Accuracy | Sensitivity (True Positive Rate) | Specificity (False Positive Rate) | MCC |
---|---|---|---|---|
24 | 0.947 | 0.840 | 0.979 | 0.847 |
48 | 0.913 | 0.708 | 0.977 | 0.748 |
72 | 0.867 | 0.556 | 0.964 | 0.605 |
96 | 0.858 | 0.540 | 0.955 | 0.571 |
120 | 0.834 | 0.450 | 0.949 | 0.480 |
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Changklom, J.; Lamchuan, P.; Pornprommin, A. Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River. Water 2022, 14, 741. https://doi.org/10.3390/w14050741
Changklom J, Lamchuan P, Pornprommin A. Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River. Water. 2022; 14(5):741. https://doi.org/10.3390/w14050741
Chicago/Turabian StyleChangklom, Jiramate, Phakawat Lamchuan, and Adichai Pornprommin. 2022. "Salinity Forecasting on Raw Water for Water Supply in the Chao Phraya River" Water 14, no. 5: 741. https://doi.org/10.3390/w14050741