Environmental Science and Pollution Research

, Volume 26, Issue 29, pp 30524–30532 | Cite as

Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes

  • Wenguang Luo
  • Senlin ZhuEmail author
  • Shiqiang Wu
  • Jiangyu Dai
Short Research and Discussion Article


Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency for the National Lakes Assessment in the continental USA were analyzed. Statistical analysis showed that water quality variables in natural lakes have strong patterns of autocorrelations than man-made lakes, indicating the perturbation of anthropogenic stresses on man-made lake ecosystems. Meanwhile, adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean–clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC) were implemented to model CHLA in lakes, and modeling results were compared with the multilayer perceptron neural network models (MLPNN). Results showed that ANFIS_FC models outperformed other models for natural lakes, while for man-made lakes, MLPNN models performed the best. ANFIS_GP models have the lowest accuracies in general. The results indicated that ANFIS models can be screening tools for an overall estimation of CHLA levels of lakes in large scales, especially for natural lakes.


Artificial intelligence Chlorophyll-a Natural lakes Man-made lakes MLPNN ANFIS 


Funding information

This work was jointly funded by the National Key R&D Program of China (2018YFC0407203, 2016YFC0401506), the China Postdoctoral Science Foundation (2018 M640499), the funding of the State Key Laboratory of Eco-hydraulics in Northwest Arid Region Xi’an University of Technology (2018KFKT-7), and the research project from Nanjing Hydraulic Research Institute (Y118009).


  1. Ahn CY, Oh HM, Park YS (2011) Evaluation of environmental factors on cyanobacterial bloom in eutrophic reservoir using artificial neural networks. J Phycol 47(3):495–504CrossRefGoogle Scholar
  2. Bachmann RW, Hoyer MV, Croteau AC, Canfield DE Jr (2017) Factors related to Secchi depths and their stability over time as determined from a probability sample of US lakes. Environ Monit Assess 189:206CrossRefGoogle Scholar
  3. Binzer A, Guill C, Rall BC, Brose U (2016) Interactive effects of warming, eutrophication and size structure: impacts on biodiversity and food-web structure. Glob Chang Biol 22(1):220–227CrossRefGoogle Scholar
  4. Çamdevýren H, Demýr N, Kanik A, Keskýn S (2005) Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecol Model 108(4):581–589CrossRefGoogle Scholar
  5. Chen Q, Guan T, Yun L, Li R, Recknagel F (2015) Online forecasting chlorophyll-a concentrations by an auto-regressive integrated moving average model: feasibilities and potentials. Harmful Algae 43:58–65CrossRefGoogle Scholar
  6. Cho KH, Kang J, Ki SJ, Kang Y, Cha SM, Kim JH (2009) Determination of the optimal parameters in regression models for the prediction of chlorophyll-a: a case study of the Yeongsan reservoir, Korea. Sci Total Environ 407(8):2536–2545CrossRefGoogle Scholar
  7. Cho S, Lim B, Jung J, Kim S, Chae H, Park J, Park S, Park JK (2014) Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network. Measurement 53:224–233CrossRefGoogle Scholar
  8. Hadzima-Nyarko M, Rabi A, Šperac M (2014) Implementation of artificial neural networks in modeling the water-air temperature relationship of the river Drava. Water Resour Manag 28:1379–1394CrossRefGoogle Scholar
  9. Hamilton HA, Ivanova D, Stadler K, Merciai S, Schmidt J, van Zelm R, Moran D, Wood R (2018) Trade and the role of non-food commodities for global eutrophication. Nature Sustainability 1:314–321CrossRefGoogle Scholar
  10. Hautier Y, Seabloom EW, Borer ET et al (2014) Eutrophication weakens stabilizing effects of diversity in natural grasslands. Nature 588:521–525CrossRefGoogle Scholar
  11. Heddam S (2014) Modeling hourly dissolved oxygen concentration (DO) using two different adaptive neuro-fuzzy inference systems (ANFIS): a comparative study. Environ Monit Assess 186:587–619Google Scholar
  12. Heddam S (2016) Multilayer perceptron neural network-based approach for modelling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA. Environ Sci Pollut Res 23(17):17210–17225CrossRefGoogle Scholar
  13. Huang J, Gao J (2017) An ensemble simulation approach for artificial neural network: an example from chlorophyll a simulation in Lake Poyang, China. Ecological Informatics 37:52–58CrossRefGoogle Scholar
  14. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23(3):665–685CrossRefGoogle Scholar
  15. Jeong K, Kim D, Joo G (2006) River phytoplankton prediction model by artificial neural network: model performance and selection of input variables to predict time-series phytoplankton proliferations in a regulated river system. Ecological Informatics 1(3):235–245CrossRefGoogle Scholar
  16. Kane DD, Conroy JD, Richards RP, Baker DB, Culver DA (2014) Re-eutrophication of Lake Erie: correlations between tributary nutrient loads and phytoplankton biomass. J Great Lakes Res 40(3):496–501CrossRefGoogle Scholar
  17. Karul C, Soyupak S, Cilesiz AF, Akbay N, Germen E (2000) Case studies on the use of neural networks in eutrophication modeling. Ecol Model 134:145–152CrossRefGoogle Scholar
  18. Kim HG, Hong S, Jeong K, Kim D, Joo G (2019) Determination of sensitive variables regardless of hydrological alteration in artificial neural network model of chlorophyll a: case study of Nakdong River. Ecol Model 398:67–76CrossRefGoogle Scholar
  19. Kuo J, Hsieh M, Lung W, She N (2007) Using artificial neural network for reservoir eutrophication prediction. Ecol Model 200(1–2):171–177CrossRefGoogle Scholar
  20. Li W, Qi B, Zhu G (2014) Forecasting short-term cyanobacterial blooms in Lake Taihu, China, using a coupled hydrodynamic–algal biomass model. Ecohydrology 7(2):794–802CrossRefGoogle Scholar
  21. Liu Y, Guo H, Yang P (2010) Exploring the influence of lake water chemistry on chlorophyll a: a multivariate statistical model analysis. Ecol Model 221(4):681–688CrossRefGoogle Scholar
  22. Liu Y, Xi D, Li Z (2015) Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction: a case study of the Yuqiao reservoir, China. PLoS One 10(3):e0119082CrossRefGoogle Scholar
  23. Lu F, Chen Z, Liu W, Shao H (2016) Modeling chlorophyll-a concentrations using an artificial neural network for precisely eco-restoring lake basin. Ecol Eng 95:422–429CrossRefGoogle Scholar
  24. McCrackin ML, Jones HP, Jones PC, Moreno-Mateos D (2017) Recovery of lakes and coastal marine ecosystems from eutrophication: a global meta-analysis. Limnol Oceanogr 62:507–518CrossRefGoogle Scholar
  25. Mulia IE, Tay H, Roopsekhar K, Tkalich P (2013) Hybrid ANN–GA model for predicting turbidity and chlorophyll-a concentrations. J Hydro Environ Res 7(4):279–299CrossRefGoogle Scholar
  26. Najah A, El-Shafie A, Karim OA, El-Shafie AH (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ Sci Pollut Res 21(3):1658–1670CrossRefGoogle Scholar
  27. Park Y, Cho KH, Park J, Cha SM, Kim JH (2015) Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Sci Total Environ 502:31–41CrossRefGoogle Scholar
  28. Recknagel F, French M, Harkonen P, Yabunaka K (1997) Artificial neural network approach for modelling and prediction of algal blooms. Ecol Model 96:11–28CrossRefGoogle Scholar
  29. Sinshaw TA, Surbeck CQ, Yasarer H, Najjar Y (2019) Artificial neural network for prediction of total nitrogen and phosphorus in US Lakes. J Environ Eng 145(6):04019032CrossRefGoogle Scholar
  30. Terauchi G, Tsujimoto R, Ishizaka J, Nakata H (2014) Preliminary assessment of eutrophication by remotely sensed chlorophyll-a in Toyama Bay, the sea of Japan. J Oceanogr 70(2):175–184CrossRefGoogle Scholar
  31. Tian W, Liao Z, Zhang J (2017) An optimization of artificial neural network model for predicting chlorophyll dynamics. Ecol Model 364:42–52CrossRefGoogle Scholar
  32. Trolle D, Hamilton DP, Pilditch CA, Duggan IC, Jeppesen E (2011) Predicting the effects of climate change on trophic status of three morphologically varying lakes: implications for lake restoration and management. Environ Model Softw 26(4):354–370CrossRefGoogle Scholar
  33. USEPA (2009) National Lakes Assessment: a collaborative survey of the nation’s lakes. EPA 841-R-09-001. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development, Washington, D.C.Google Scholar
  34. USEPA (2016) National Lakes Assessment 2012: a collaborative survey of lakes in the United States. EPA 841-R-16-113. U.S. Environmental Protection Agency, Office of Water and Office of Research and Development, Washington, D.C.Google Scholar
  35. Whigham PA, Recknagel F (2001) Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms. Ecol Model 146(1–3):243–251CrossRefGoogle Scholar
  36. Wu N, Huang J, Schmalz B, Fohrer N (2014) Modeling daily chlorophyll a dynamics in a German lowland river using artificial neural networks and multiple linear regression approaches. Limnology 15(1):47–56CrossRefGoogle Scholar
  37. Yabunaka K, Hosomi M, Murakami A (1997) Novel application of a back-propagation artificial neural network model formulated to predict algal bloom. Water Sci Technol 36(5):89–97CrossRefGoogle Scholar
  38. Zhou L, Ma W, Zhang H, Li L, Tang L (2015) Developing a PCA–ANN model for predicting chlorophyll a concentration from field hyperspectral measurements in Dianshan Lake, China. Water Qual Expo Health 7(4):591–602CrossRefGoogle Scholar
  39. Zhu S, Heddam S, Nyarko EK, Hadzima-Nyarko M, Piccolroaz S, Wu S (2019) Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environ Sci Pollut Res 26(1):402–420CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Water Resources and Hydropower Engineering ScienceWuhan UniversityWuhanChina
  2. 2.State Key Laboratory of Hydrology-Water Resources and Hydraulic EngineeringNanjing Hydraulic Research InstituteNanjingChina

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