Apply Machine Learning to Predict Saltwater Intrusion in the Ham Luong River, Ben Tre Province
Main Article Content
Abstract
Saltwater intrusion is a major problem particularly in the Mekong Delta, Việt Nam. In order to better manage the salinity problem, it is important to be able to predict the saltwater intrusion in rivers. The objective of this research is to apply several machine learning algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), Artificial Neural Networks (ANN) for predicting the saltwater intrusion in Ham Luong River, Ben Tre Province. The input data is is composed of 207 weekly saltwater intrusion data points from 2012 to 2020. Yearly salinity was measured during the 23 weeks of the dry season, from January to June. The Nash - Sutcliffe efficiency coefficient (NSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) are used to evaluate the performances of machine learning algorithms. The research results indicated that the ANN model achieved a high performance for salinity forecasting with NSE = 0.907, RMSE = 0.11, MAE = 0.08 for training period, NSE = 0.842, RMSE = 1.16, MAE = 0.11 for testing period. The findings of this study suggest that the ANN algorithm is a promising tool to forecast salinity in Ham Luong River.
Keywords:
Artificial intelligence, climate change, Mekong Delta, saltwater intrusion.
References
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Vol. 742, 2020, pp. 140596, https://doi.org/10.1016/j.scitotenv.2020.140596.
[3] H. T. Nguyen, A. D. Gupta, Assessment of Water Resources and Salinity Intrusion in the Mekong Delta, Water Int., Vol. 26, No. 1, 2001, pp. 86-95, https://doi.org/10.1080/02508060108686889.
[4] A. D. Tran, L. P. Hoang, M. D. Bui,
P. Rutschmann, Simulating Future Flows and Salinity Intrusion Using Combined One-and Two-dimensional Hydrodynamic Modelling-the Case of Hau River, Vietnamese Mekong Delta, Water,
Vol. 10, No. 7, 2018, pp. 897, https://doi.org/10.3390/w10070897.
[5] V. B. Doan, S. A. Kantoush, M. Saber, N. P. Mai, S. Maskey, D. T. Phong, T. Sumi, Long-term Alterations of Flow Regimes of the Mekong River and Adaptation Strategies for the Vietnamese Mekong Delta, J. Hydrol. Reg. Stud., Vol. 32, 2020, pp. 100742, https://doi.org/10.1016/j.ejrh.2020.100742.
[6] A. Smajgl, T. Q. Toan, D. K. Nhan, J. Ward, N. H. Trung, L. Q. Tri, P. V. Tri, P. T. Vu, Responding to Rising Sea Levels in the Mekong Delta, Nat. Clim. Change, Vol. 5, No. 2, 2015, pp. 167-174, https://doi.org/10.1038/nclimate2469.
[7] H. Apel, M. Khiem, N. H. Quan, T. Q. Toan, Brief Communication: Seasonal Prediction of Salinity Intrusion in the Mekong Delta, Nat. Hazards Earth Syst. Sci., Vol. 20, No. 6, 2020, pp. 1609-1616, https://doi.org/10.5194/nhess-2019-276.
[8] M. H. Rahman, T. Lund, I. Bryceson, Salinity Impacts on Agro-biodiversity in three Coastal, Rural Villages of Bangladesh, Ocean Coast Manag., Vol. 54, No. 6, 2011, pp. 455-468, https://doi.org/10.1016/j.ocecoaman.2011.03.003.
[9] A. C. Ross, C. A. Stock, An Assessment of the Predictability of Column Minimum Dissolved Oxygen Concentrations in Chesapeake Bay using A Machine Learning Model, Estuar. Coast. Shelf Sci., Vol 221, 2019, pp. 53-65, https://doi.org/10.1016/j.ecss.2019.03.007.
[10] K. Lin, P. Lu, C.Y. Xu, X. Yu, T. Lan, X. Chen, Modeling Saltwater Intrusion using an Integrated Bayesian model Averaging Method in the Pearl River Delta, J. Hydroinformatics, Vol. 21, No. 6, 2019, pp. 1147-1162, https://doi.org/10.2166/hydro.2019.073.
[11] ] Z. Liang, R. Zou, X. Chen, T. Ren, H. Su, Y. Liu, Simulate the Forecast Capacity of a Complicated Water Quality Model using the Long Short-term Memory Approach, J. Hydrol., Vol. 581, 2020,
pp. 124432.
[12] J. Zhang, Y. Zhu, X. Zhang, M. Ye, J. Yang, Developing a Long Short-Term Memory (LSTM) Based Model for Predicting Water Table Depth in Agricultural Areas, J. Hydrol., Vol. 561, 2018,
pp. 918-929, https://doi.org/10.1016/j.jhydrol.2018.04.065.
[13] T. T. H. Phan, X. H. Nguyen, Combining Statistical Machine Learning Models with ARIMA for Water Level Forecasting: The Case of the Red River, Adv. Water Resour., Vol. 142, 2020,
pp. 103656-103692, https://doi.org/10.1016/j.advwatres.2020.103656.
[14] S. Palani, S.Y. Liong, P. Tkalich, An ANN Application for Water Quality Forecasting, Mar. Pollut. Bull., Vol. 56, No. 9, 2008, pp. 1586-1597, https://doi.org/10.1016/j.marpolbul.2008.05.021.
[15] J. M. Hunter, H. R. Maier, M. S. Gibbs, E. R. Foale, N. A. Grosvenor, N. P. Harders, T. C. K. Miller, Framework for Developing Hybrid Process-driven, Artificial Neural Network and Regression Models for Salinity Prediction In River Systems, Hydrol. Earth Syst. Sci., Vol. 22, No. 5, 2018, pp. 2987-3006, https://doi.org/10.5194/hess-22-2987-2018.
[16] A. Stelzl, M. Pointl, D. F. Hanusch, Estimating Future Peak Water Demand with a Regression Model Considering Climate Indices, Water,
Vol. 13, No. 14, 2021, pp. 1912, https://doi.org/10.3390/w13141912.
[17] T. D. H. Le, M. Kattwinkel, K. Schützenmeister,
J. R. Olson, C. P. Hawkins, R. B. Schäfer, Predicting Current and Future Background Ion Concentrations in German Surface Water Under Climate Change, Philos. Trans. R. Soc. Lond., B. Biol. Sci., Vol. 374, No. 1764, 2019, pp. 20180004, https://doi.org/10.1098/rstb.2018.0004.
[18] G. Zhu, S. Zhang, Y. Bian, A. S. Hursthouse, Multi-linear Regression Model for Chlorine Consumption By Waters, Environ. Eng. Res.,
Vol. 26, No. 4, 2020, pp. 200402, https://doi.org/10.4491/eer.2020.402.
[19] T. T. Tran, Q. X. Ngo, H. H. Ha, N. P. Nguyen, Short-term Forecasting of Salinity Intrusion in Ham Luong River, Ben Tre Province using Simple Exponential Smoothing Method, J. Viet. Env.,
Vol. 11, 2019, pp. 43-50, https://doi.org/10.13141/jve.vol11.no2.pp43-50.
[20] T. T. Tran, L. N. Duy,T. L. Pham, T. M. Y. Nguyen, T. H. Y. Tran, X. Q. Ngo, V. T. Lam,
H. P. Ngoc, Performance Evaluation of Autoregressive Integrated Moving Average Models for Forecasting Saltwater Intrusion Into Mekong River Estuaries of Vietnam, Vietnam Journal of Earth Sciences, Vol. 43, No. 4, 2021,
pp. 428-443,
https://doi.org/10.15625/2615-9783/16440.
[21] H. Liu, G. X. Sun, R. X. Cao, The Application of GM (1, 1) Dynamic Model in the Forecast of Groundwater Level In Wujiang City, J. Geol. Hazards Environ. Preserv., Vol. 19, No. 3, pp. 47-51.
[22] M. Pan, H. Zhou, J. Cao, Y. Liu, J. Hao, S. Li,
C.H. Chen, Water Level Prediction Model Based on GRU and CNN, IEEE Access, Vol. 8, 2008,
pp. 60090-60100, https://doi.org/10.1109/ACCESS.2020.2982433.
[23] M. Frigge, D. C. Hoaglin, B. Iglewicz, Some Implementations of the Boxplot, Am Stat., Vol. 43, No. 1, 1989, pp. 50-54, https://doi.org/10.2307/2685173.
[24] P. Geurts, D. Ernst, L. Wehenkel, Extremely Randomized Trees, Mach. Learn., Vol. 63, No. 1, 2006, pp. 3-42, https://doi.org/10.1007/s10994-006-6226-1.
[25] J. Adamowski, H. Fung Chan, S. O. Prasher, B. O. Zielinski, A. Sliusarieva, Comparison of Multiple Linear and Nonlinear Regression, Autoregressive Integrated Moving Average, Artificial Neural Network, and Wavelet Artificial Neural Network Methods for Urban Water Demand Forecasting in Montreal, Canada, Water Resour. Res., Vol. 48, No. 1, 2012, pp. 1528, https://doi.org/10.1029/2010WR009945.
[26] L. Breiman. Random Forests, Mach. Learn,
Vol. 45, No. 1, 2001, pp. 5-32.
[27] T. Francke, J. L. Tarazón, B. Schroder, Estimation of Suspended Sediment Concentration and Yield Using Linear Models, Random Forests and Quantile Regression Forests, Hydrol. Process,
No. 22, Vo. 25, 2008, pp. 4892, https://doi.org/10.1002/hyp.7110.
[28] T. T. H. Nguyen, M. T. Doan, Apply the Random Forest Classification Algorithm to Build Building Land Use/Carpet Cover Map of Dak Lak Province Based on Landsat 8 OLI satellite image, Journal of Agriculture & Rural Development, Vol. 13, 2018, pp. 122-129 (in Vietnamese).
[29] H. Q. Nguyen, N. T. Ha, T. L. Pham, Inland Harmful Cyanobacterial Bloom Prediction in the Eutrophic Tri An Reservoir Using Satellite Band Ratio and Machine Learning Approaches, Environ. Sci. Pollut. Res., Vol. 27, No. 9, 2020,
pp. 9135-9151,
https://doi.org/10.1007/s11356-019-07519-3.
[30] J. E. Nash, J. V. Sutcliffe, River Flow Forecasting Through Conceptual Models Part I – a Discussion of Principles, J. Hydrol., Vol. 10, 1970,
pp. 282-290,
https://doi.org/10.1016/0022-1694(70)90255-6.
[31] D. N. Moriasi, M. W. Gitau, N. Pai, P. Daggupati, Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria, Trans. ASABE, Vol. 58, 2015, pp. 1763-1785, https://doi.org/10.13031/trans.58.10715.
[32] S. B. Qiu, B. Tang, Application of Mutiple Linear Regression Analysis in Polymer Modified Mortar Quality Control, in Proceedings of the 2nd International Conference on Electronic and Mechanical Engineering and Information Technology, 2012, pp. 1124-1127.
[33] S. I. Abba, S. J. Hadi, J. Abdullahi, River Water Modelling Prediction Using Multi-Linear Regression, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Techniques, Procedia Comput. Sci., Vol. 120, 2017, pp. 75-82, https://doi.org/10.1016/j.procs.2017.11.212.
[34] W. B. Chen, W.C. Liu, Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models, Adv. Artif. Neural Syst., Vol. 2015, 2015, https://doi.org/10.1155/2015/521721.
[35] L. A. Garcia, A. A. Eldeiry, Evaluating Linear and Nonlinear Regression Models in Mapping Soil Salinity, Int. J. Res. Agric. For., Vol. 7, No. 3, 2020, pp. 21-34, http://ijraf.org/papers/v7-i3/4.pdf.
[36] S. Wolff, F. O'Donncha, B. Chen, Statistical and Machine Learning Ensemble Modelling to Forecast Sea Surface Temperature, J. Mar. Syst., Vol. 208, 2018, pp. 103347, https://doi.org/10.1016/j.jmarsys.2020.103347.
[37] A. Lal, B. Datta, Application of the Group Method of Data Handling and Variable Importance Analysis for Prediction and Modelling of Saltwater Intrusion Processes in Coastal Aquifers, Neural. Comput. Appl., Vol. 33, 2020, pp. 4179-4190, https://doi.org/10.1007/s00521-020-05232-8.
[38] J. Lago, F. D. Ridder, B. D. Schutter, Forecasting Spot Electricity Prices: Deep Learning
Approaches and Empirical Comparison of Traditional Algorithms, Appl. Energy, Vol. 221, 2015, pp. 386-405, https://doi.org/10.1016/j.apenergy.2018.02.069.
[39] A. Schmidt, D. B. Mainwaring, D. A. Maguire, Development of a Tailored Combination of
Machine Learning Approaches to Model Volumetric Soil Water Content Within A Mesic Forest in the Pacific Northwest, J. Hydrol.,
Vol. 588, 2020, pp. 125044, https://doi.org/10.1016/j.jhydrol.2020.125044.
[40] M. S. Lola, M. N. A. Ramlee, G. S. Gunalan, N. H. Zainuddin, R. Zakariya, M. Idris, I. Khalil, Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll-a at the Offshore Kuala Terengganu, Terengganu, Open Journal of Statistics, Vol. 6. No. 5, 2016, pp. 789-804, https://doi.org/10.4236/ojs.2016.65065.
[41] H. Ebrahimi, T. Rajaee, Simulation of Groundwater Level Variations Using Wavelet Combined with Neural Network, Linear Regression and Support Vector Machine, Glob. Planet Change, Vol. 148, 2017, pp. 181-191, https://doi.org/10.1016/j.gloplacha.2016.11.014.
[42] A. D. Tran, L. P. Hoang, M. D. Bui,
P. Rutschmann, Simulating Future Flows and Salinity Intrusion Using Combined One- and Two-Dimensional Hydrodynamic Modelling-The Case of Hau River, Vietnamese Mekong Delta, Water, Vol. 10, 2018, pp. 897-917,