Abstract
Surface water eutrophication due to excessive nutrients has become a major environmental problem around the world in the past few decades. Among these nutrients, nitrogen and phosphorus are two of the most important harmful cyanobacterial bloom (HCB) drivers. A reliable prediction of these parameters, therefore, is necessary for the management of rivers, lakes, and reservoirs. The aim of this study is to test the suitability of the powerful machine learning (ML) algorithm, random forest (RF), to provide information on water quality parameters for the Tri An Reservoir (TAR). Three species of nitrogen and phosphorus, including nitrite (N-NO2−), nitrate (N-NO3−), and phosphate (P-PO43−), were empirically estimated using the field observation dataset (2009–2014) of six surrogates of total suspended solids (TSS), total dissolved solids (TDS), turbidity, electrical conductivity (EC), chemical oxygen demand (COD), and biochemical oxygen demand (BOD5). Field data measurement showed that water quality in the TAR was eutrophic with an up-trend of N-NO3− and P-PO43− during the study period. The RF regression model was reliable for N-NO2−, N-NO3−, and P-PO43− prediction with a high R2 of 0.812–0.844 for the training phase (2009–2012) and 0.888–0.903 for the validation phase (2013–2014). The results of land use and land cover change (LUCC) revealed that deforestation and shifting agriculture in the upper region of the basin were the major factors increasing nutrient loading in the TAR. Among the meteorological parameters, rainfall pattern was found to be one of the most influential factors in eutrophication, followed by average sunshine hour. Our results are expected to provide an advanced assessment tool for predicting nutrient loading and for giving an early warning of HCB in the TAR.
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References
Asian Development Bank (ADB). (2009). Water: vital for Viet Nam’s future. Vietnam: Ha Noi.
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
Berrendero, E., Valiente, E. F., Perona, E., Gómez, C. L., Loza, V., Muñoz-Martín, M. Á., & Mateo, P. (2016). Nitrogen fixation in a non-heterocystous cyanobacterial mat from a mountain river. Scientific Reports, 6(1), 30920. https://doi.org/10.1038/srep30920.
Bi, W., Weng, B., Yuan, Z., Ye, M., Zhang, C., Zhao, Y., Yan, D., & Xu, T. (2018). Evolution characteristics of surface water quality due to climate change and LUCC under scenario simulations: a case study in the Luanhe River Basin. International Journal of Environmental Research and Public Health, 15(8), 1724. https://doi.org/10.3390/ijerph15081724.
Blix, K., & Eltoft, T. (2018). Machine learning automatic model selection algorithm for oceanic chlorophyll-a content retrieval. Remote Sensing, 10(5), 775. https://doi.org/10.3390/rs10050775.
Breiman, L. (2001). Random forest. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.
Bresciani, M., Cazzaniga, I., Austoni, M., Sforzi, T., Buzzi, F., Morabito, G., & Giardino, C. (2018). Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8. Hydrobiologia, 824(1), 197–214. https://doi.org/10.1007/s10750-017-3462-2.
Bridgewater, L. L., Baird, R. B., Eaton, A. D., Rice, E. W., & American Public Health Association, American Water Works Association, & Water Environment Federation (Eds.). (2017). Standard methods for the examination of water and wasterwater (23rd ed.). Washington, DC: American Public Health Association.
Brito, D., Neves, R., Branco, M., Prazeres, Â., Rodrigues, S., Gonçalves, M., & Ramos, T. (2019). Assessing water and nutrient long-term dynamics and loads in the Enxoé temporary river basin (Southeast Portugal). Water, 11(2), 354. https://doi.org/10.3390/w11020354.
Bui, M.-H., Pham, T.-L., & Dao, T.-S. (2017). Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network. Marine and Freshwater Research, 68(11), 2070. https://doi.org/10.1071/MF16327.
Byrareddy, V., Kouadio, L., Mushtaq, S., & Stone, R. (2019). Sustainable production of robusta coffee under a changing climate: a 10-year monitoring of fertilizer management in coffee farms in Vietnam and Indonesia. Agronomy, 9(9), 499. https://doi.org/10.3390/agronomy9090499.
Calvi, C., Dapeña, C., Martinez, D. E., & Quiroz Londoño, O. M. (2018). Relationship between electrical conductivity, 18O of water and NO3 content in different streamflow stages. Environmental Earth Sciences, 77(6), 248. https://doi.org/10.1007/s12665-018-7427-1.
Carlsson, H., Aspegren, H., & Hilmer, A. (1996). Interactions between wastewater quality and phosphorus release in the anaerobic reactor of the EBPR process. Water Research, 30(6), 1517–1527. https://doi.org/10.1016/0043-1354(95)00333-9.
Castrillo, M., & García, Á. L. (2020). Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods. Water Research, 172, 115490. https://doi.org/10.1016/j.watres.2020.115490.
Chen, W.-B., & Liu, W.-C. (2015). Water quality modeling in reservoirs using multivariate linear regression and two neural network models. Advances in Artificial Neural Systems, 2015, 1–12. https://doi.org/10.1155/2015/521721.
Corwin, D. L., Lesch, S. M., Oster, J. D., & Kaffka, S. R. (2006). Monitoring management-induced spatio–temporal changes in soil quality through soil sampling directed by apparent electrical conductivity. Geoderma, 131(3), 369–387. https://doi.org/10.1016/j.geoderma.2005.03.014.
Daphne, L., Djati Utomo, H., & Kenneth, L. (2011). Correlation between turbidity and total suspended solids in Singapore rivers. Journal of Water Sustainability, 1, 313–322.
Davies-Colley, R. J., Hickey, C. W., & Quinn, J. M. (1995). Organic matter, nutrients, and optical characteristics of sewage lagoon effluents. New Zealand Journal of Marine and Freshwater Research, 29(2), 235–250. https://doi.org/10.1080/00288330.1995.9516657.
Dubey, D., & Dutta, V. (2020). Nutrient enrichment in lake ecosystem and its effects on algae and macrophytes. In V. Shukla & N. Kumar (Eds.), Environmental concerns and sustainable development (pp. 81–126). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-13-6358-0_5.
Durbin–Watson Test. (2008). In The concise encyclopedia of statistics (pp. 173–175). New York: Springer New York. https://doi.org/10.1007/978-0-387-32833-1_122.
Fawagreh, K., Gaber, M. M., & Elyan, E. (2014). Random forests: from early developments to recent advancements. Systems Science & Control Engineering, 2(1), 602–609. https://doi.org/10.1080/21642583.2014.956265.
García, N. P. J., García-Gonzalo, E., Alonso Fernández, J. R., & Díaz Muñiz, C. (2019). Water eutrophication assessment relied on various machine learning techniques: a case study in the Englishmen Lake (Northern Spain). Ecological Modelling, 404, 91–102. https://doi.org/10.1016/j.ecolmodel.2019.03.009.
Grattan, L. M., Holobaugh, S., & Morris, J. G. (2016). Harmful algal blooms and public health. Harmful Algae, 57, 2–8. https://doi.org/10.1016/j.hal.2016.05.003.
Heisler, J., Glibert, P. M., Burkholder, J. M., Anderson, D. M., Cochlan, W., Dennison, W. C., Dortch, Q., Gobler, C. J., Heil, C. A., Humphries, E., Lewitus, A., Magnien, R., Marshall, H. G., Sellner, K., Stockwell, D. A., Stoecker, D. K., & Suddleson, M. (2008). Eutrophication and harmful algal blooms: a scientific consensus. Harmful Algae, 8(1), 3–13. https://doi.org/10.1016/j.hal.2008.08.006.
Herschy, R. W. (2012). Lake sediments. In L. Bengtsson, R. W. Herschy, & R. W. Fairbridge (Eds.), Encyclopedia of lakes and reservoirs. Dordrecht: Encyclopedia of Earth Sciences Series. Springer. https://doi.org/10.1007/978-1-4020-4410-6_26.
Hollister, J. W., Milstead, W. B., & Kreakie, B. J. (2016). Modeling lake trophic state: a random forest approach. Ecosphere, 7(3), e01321. https://doi.org/10.1002/ecs2.1321.
JICA. (n.d.) (1996). The master plan study on Dong Nai River and surrounding basins water resources development: final report: Vol. 4. Appendix II: Topography and geology, appendix III: Meteorology and hydrology. Japan International Cooperation Agency: Nippon Koei Co., Ltd. https://openjicareport.jica.go.jp/617/617/617_123_11309523.html. Accessed 20 Sep 2017.
Jones, K. B., Neale, A. C., Nash, M. S., Van Remortel, R. D., Wickham, J. D., Riitters, K. H., & O’Neill, R. V. (2001). Predicting nutrient and sediment loadings to streams from landscape metrics: a multiple watershed study from the United States Mid-Atlantic region. Landscape Ecology, 16(4), 301–312. https://doi.org/10.1023/A:1011175013278.
Jones, J. R., Knowlton, M. F., Obrecht, D. V., & Cook, E. A. (2004). Importance of landscape variables and morphology on nutrients in Missouri reservoirs. Canadian Journal of Fisheries and Aquatic Sciences. https://doi.org/10.1139/f04-088.
Jung, K., Bae, D.-H., Um, M.-J., Kim, S., Jeon, S., & Park, D. (2020). Evaluation of nitrate load estimations using neural networks and canonical correlation analysis with k-fold cross-validation. Sustainability, 12(1), 400. https://doi.org/10.3390/su12010400.
Keller, S., Maier, P., Riese, F., Norra, S., Holbach, A., Börsig, N., Wilhelms, A., Moldaenke, C., Zaake, A., & Hinz, S. (2018). Hyperspectral data and machine learning for estimating CDOM, chlorophyll a, diatoms, green algae and turbidity. International Journal of Environmental Research and Public Health, 15(9), 1881. https://doi.org/10.3390/ijerph15091881.
Kim, K.-S., Yoo, J.-S., Kim, S., Lee, H. J., Ahn, K.-H., & Kim, I. S. (2007). Relationship between the electric conductivity and phosphorus concentration variations in an enhanced biological nutrient removal process. Water Science and Technology: A Journal of the International Association on Water Pollution Research, 55(1–2), 203–208. https://doi.org/10.2166/wst.2007.053.
Kim, R. J., Loucks, D. P., & Stedinger, J. R. (2012). Artificial neural network models of watershed nutrient loading. Water Resources Management, 26(10), 2781–2797. https://doi.org/10.1007/s11269-012-0045-x.
Lam Dong Department of Statistic (2018). Lam Dong statistical yearbook 2018. Statistical Publishing House (Vietnam). Accessed 2 Jan 2020.
Lewis, W. M., Wurtsbaugh, W. A., & Paerl, H. W. (2011). Rationale for control of anthropogenic nitrogen and phosphorus to reduce eutrophication of inland waters. Environmental Science & Technology, 45(24), 10300–10305. https://doi.org/10.1021/es202401p.
Li, X., Huang, T., Ma, W., Sun, X., & Zhang, H. (2015). Effects of rainfall patterns on water quality in a stratified reservoir subject to eutrophication: implications for management. Science of The Total Environment, 521–522, 27–36. https://doi.org/10.1016/j.scitotenv.2015.03.062.
Li, X., Sha, J., & Wang, Z.-L. (2018). Application of feature selection and regression models for chlorophyll-a prediction in a shallow lake. Environmental Science and Pollution Research, 25(20), 19488–19498. https://doi.org/10.1007/s11356-018-2147-3.
Lou, I., Xie, Z., Ung, W. K., & Mok, K. M. (2016). Freshwater algal bloom prediction by extreme learning machine in Macau storage reservoirs. Neural Computing and Applications, 27(1), 19–26. https://doi.org/10.1007/s00521-013-1538-0.
Lu, J., Zhu, B., Struewing, I., Xu, N., & Duan, S. (2019). Nitrogen–phosphorus-associated metabolic activities during the development of a cyanobacterial bloom revealed by metatranscriptomics. Scientific Reports, 9(1), 2480. https://doi.org/10.1038/s41598-019-38481-2.
Marttila, H., & Kløve, B. (2009). Retention of Sediment and Nutrient Loads with Peak Runoff Control. Journal of Irrigation and Drainage Engineering, 135(2), 210–216.
Marttila, H., & Kløve, B. (2012). Use of turbidity measurements to estimate suspended solids and nutrient loads from peatland forestry drainage. Journal of Irrigation and Drainage Engineering, 138(12), 1088–1096. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000509.
Mas, J. F., & Flores, J. J. (2008). The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29(3), 617–663. https://doi.org/10.1080/01431160701352154.
Meyfroidt, P., Vu, T. P., & Hoang, V. A. (2013). Trajectories of deforestation, coffee expansion and displacement of shifting cultivation in the Central Highlands of Vietnam. Global Environmental Change, 23(5), 1187–1198. https://doi.org/10.1016/j.gloenvcha.2013.04.005.
Mohapatra, N., Shreya, K., & Chinmay, A. (2020). Optimization of the random forest algorithm. In S. Borah, V. Emilia Balas, & Z. Polkowski (Eds.), Advances in data science and management (Vol. 37, pp. 201–208). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-0978-0_19.
Morris, J. G. (1999). An emerging public health problem with possible links to human stress on the environment. Annual Review of Energy and the Environment, 24(1), 367–390. https://doi.org/10.1146/annurev.energy.24.1.367.
Mu, M., Wu, C., Li, Y., Lyu, H., Fang, S., Yan, X., Liu, G., Zheng, Z., Du, C., & Bi, S. (2019). Long-term observation of cyanobacteria blooms using multi-source satellite images: a case study on a cloudy and rainy lake. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-019-04522-6.
Nguyen, H.-Q., Ha, N.-T., & Pham, T.-L. (2020). Inland harmful cyanobacterial bloom prediction in the eutrophic Tri An Reservoir using satellite band ratio and machine learning approaches. Environmental Science and Pollution Research, 27, 9135–9151. https://doi.org/10.1007/s11356-019-07519-3.
Oyebode, O., & Stretch, D. (2019). Neural network modeling of hydrological systems: a review of implementation techniques. Natural Resource Modeling, 32(1), e12189. https://doi.org/10.1111/nrm.12189.
Park, J.-H., Inam, E., Abdullah, M. H., Agustiyani, D., Duan, L., Hoang, T. T., Kim, K.-W., Kim, S. D., Nguyen, M. H., Pekthong, T., Sao, V., Sarjiya, A., Savathvong, S., Sthiannopkao, S., Keith Syers, J., & Wirojanagud, W. (2011). Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. Journal of Hydrology, 400(3–4), 323–332. https://doi.org/10.1016/j.jhydrol.2011.01.050.
Park, Y., Cho, K. H., Park, J., Cha, S. M., & Kim, J. H. (2015). Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Science of The Total Environment, 502, 31–41. https://doi.org/10.1016/j.scitotenv.2014.09.005.
Parmar, A., Katariya, R., & Patel, V. (2019). A review on random forest: an ensemble classifier. In J. Hemanth, X. Fernando, P. Lafata, & Z. Baig (Eds.), International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018 (pp. 758–763). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-03146-6_86.
Paudel, B., Montagna, P. A., & Adams, L. (2019). The relationship between suspended solids and nutrients with variable hydrologic flow regimes. Regional Studies in Marine Science, 29, 100657. https://doi.org/10.1016/j.rsma.2019.100657.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, & Cournapeau, D. (2011). scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
Pham, T.-L., Tran, T. H. Y., Hoang, N. S., Ngo, X. Q., & Tran, T. T. (2020a). Co-occurrence of microcystin- and geosmin- producing cyanobacteria in the Tri An Reservoir, a drinking-water supply in Vietnam. Fundamental and Applied Limnology/Archiv für Hydrobiologie, 193(4), 299–311. https://doi.org/10.1127/fal/2020/1296.
Pham, T.-L., Tran, T. H. Y., Shimizu, K., Li, Q., & Utsumi, M. (2020b). Toxic cyanobacteria and microcystin dynamics in a tropical reservoir: assessing the influence of environmental variables. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-020-10826-9.
PVFCCo (2016). Polyhalite application improves coffee (Coffea robusta) yield and quality in Vietnam. International Potash Institute, e-ifc, No. 47, December 2016, pp. 12–19. https://www.ipipotash.org/uploads/udocs/e-ifc-47-dec2016-coffee-vietnam.pdf. Accessed 1 Oct 2020
Qian, S. S., Reckhow, K. H., Zhai, J., & McMahon, G. (2005). Nonlinear regression modeling of nutrient loads in streams: a Bayesian approach. Water Resources Research, 41(7). https://doi.org/10.1029/2005WR003986.
Reichwaldt, E. S., & Ghadouani, A. (2012). Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: between simplistic scenarios and complex dynamics. Water Research, 46(5), 1372–1393. https://doi.org/10.1016/j.watres.2011.11.052.
Ross, M. R. V., Topp, S. N., Appling, A. P., Yang, X., Kuhn, C., Butman, D., Simard, M. & Pavelsky, T.M. (2019). AquaSat: a data set to enable remote sensing of water quality for inland waters. Water Resources Research, 2019WR024883. https://doi.org/10.1029/2019WR024883
Schindler, D. W., Hecky, R. E., Findlay, D. L., Stainton, M. P., Parker, B. R., Paterson, M. J., Beaty, K. G., Lyng, M., & Kasian, S. E. M. (2008). Eutrophication of lakes cannot be controlled by reducing nitrogen input: results of a 37-year whole-ecosystem experiment. Proceedings of the National Academy of Sciences, 105(32), 11254–11258.
Seabold, S., & Perktold, J. (2010). Statsmodels: econometric and statistical modeling with Python. In 9th Python in Science Conference.
Shang, L. (2019). Climate change and land use/cover change impacts on watershed hydrology, nutrient dynamics – a case study in Missisquoi River watershed (Graduate College Dissertations and Theses). Vermont. Retrieved from https://scholarworks.uvm.edu/graddis/1016. Accessed 20 Jan 2020.
Shen, L. Q., Amatulli, G., Sethi, T., Raymond, P., & Domisch, S. (2020). Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework. Scientific Data, 7(1), 161. https://doi.org/10.1038/s41597-020-0478-7.
Sihag, P., Mohsenzadeh Karimi, S., & Angelaki, A. (2019). Random forest, M5P and regression analysis to estimate the field unsaturated hydraulic conductivity. Applied Water Science, 9(5), 129. https://doi.org/10.1007/s13201-019-1007-8.
Tiemann, T., Maung Aye, T., Duc Dung, N., Minh Tien, T., Fisher, M., Nalin de Paulo, E., & Oberthür, T. (2018). Crop nutrition for Vietnamese robusta coffee. Better Crops with Plant Food, 102(3), 20–23. https://doi.org/10.24047/BC102320.
Tong, S. T. Y., & Chen, W. (2002). Modeling the relationship between land use and surface water quality. Journal of Environmental Management, 66(4), 377–393. https://doi.org/10.1006/jema.2002.0593.
Trung, B., Dao, T.-S., Faassen, E., & Lürling, M. (2018). Cyanobacterial blooms and microcystins in Southern Vietnam. Toxins, 10(11), 471. https://doi.org/10.3390/toxins10110471.
Truong, N., Nguyen, H., & Kondoh, A. (2018). Land use and land cover changes and their effect on the flow regime in the upstream Dong Nai River Basin, Vietnam. Water, 10(9), 1206. https://doi.org/10.3390/w10091206.
Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11), 1225–1231. https://doi.org/10.1016/S0895-4356(96)00002-9.
Tyralis, H., Papacharalampous, G., & Langousis, A. (2019). A brief review of random forests for water scientists and practitioners and their recent history in water resources. Water, 11(5), 910. https://doi.org/10.3390/w11050910.
van Puijenbroek, P. J. T. M., Beusen, A. H. W., & Bouwman, A. F. (2019). Global nitrogen and phosphorus in urban waste water based on the shared socio-economic pathways. Journal of Environmental Management, 231, 446–456. https://doi.org/10.1016/j.jenvman.2018.10.048.
Wang, S., Qian, X., Han, B.-P., Luo, L.-C., & Hamilton, D. P. (2012). Effects of local climate and hydrological conditions on the thermal regime of a reservoir at Tropic of Cancer, in southern China. Water Research, 46(8), 2591–2604. https://doi.org/10.1016/j.watres.2012.02.014.
Wang, X., Liu, Z., Miao, J., & Zuo, N. (2015). Relationship between nutrient pollutants and suspended sediments in upper reaches of Yangtze River. Water Science and Engineering, 8(2), 121–126. https://doi.org/10.1016/j.wse.2015.04.003.
Wang, X., Gong, Z., & Pu, R. (2018). Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir, Beijing, China. Environmental Monitoring and Assessment, 190(10), 620. https://doi.org/10.1007/s10661-018-6978-7.
Wang, X., Daigger, G., de Vries, W., Kroeze, C., Yang, M., Ren, N.-Q., Liu, J., & Butler, D. (2019). Impact hotspots of reduced nutrient discharge shift across the globe with population and dietary changes. Nature Communications, 10(1), 2627. https://doi.org/10.1038/s41467-019-10445-0.
Xi, B.-D., Zhang, Y.-L., & Xu, Q.-J. (2012). Possibility of total dissolved solid as one of nutrient baselines in inner Mongolia-Xinjiang plateau. Huan Jing Ke Xue= Huanjing Kexue, 33(10), 3308–3313.
Yajima, H., & Derot, J. (2018). Application of the random forest model for chlorophyll- a forecasts in fresh and brackish water bodies in Japan, using multivariate long-term databases. Journal of Hydroinformatics, 20(1), 206–220. https://doi.org/10.2166/hydro.2017.010.
Yi, H.-S., Lee, B., Park, S., Kwak, K.-C., & An, K.-G. (2018). Short-term algal bloom prediction in Juksan weir using M5P model-tree and extreme learning machine. Environmental Engineering Research. https://doi.org/10.4491/eer.2018.245.
Zhang, H., Cui, B., Hong, J., & Zhang, K. (2011). Synergism of natural and constructed wetlands in Beijing, China. Ecological Engineering, 37(2), 128–138. https://doi.org/10.1016/j.ecoleng.2010.08.001.
Zhang, L., Huettmann, F., Zhang, X., Liu, S., Sun, P., Yu, Z., & Mi, C. (2019). The use of classification and regression algorithms using the random forests method with presence-only data to model species’ distribution. MethodsX, 6, 2281–2292. https://doi.org/10.1016/j.mex.2019.09.035.
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This study was mainly granted by the Vietnam Academy of Science and Technology (VAST) under grant number “KHCBSS.02/19-21”, and in part by the International Foundation for Science (IFS) under grant number “I-2-A-6054-1”.
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Ha, NT., Nguyen, H.Q., Truong, N.C.Q. et al. Estimation of nitrogen and phosphorus concentrations from water quality surrogates using machine learning in the Tri An Reservoir, Vietnam. Environ Monit Assess 192, 789 (2020). https://doi.org/10.1007/s10661-020-08731-2
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DOI: https://doi.org/10.1007/s10661-020-08731-2