Marine and Freshwater Research Marine and Freshwater Research Society
Advances in the aquatic sciences
RESEARCH ARTICLE

Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network

Manh-Ha Bui A B E , Thanh-Luu Pham A C and Thanh-Son Dao A D
+ Author Affiliations
- Author Affiliations

A Institute of Research and Development, Duy Tan University, 25 Quang Trung Street, Hai Chau District, Da Nang City, Vietnam.

B Department of Environmental Science, Sai Gon University, 273 An Duong Vuong Street, District 5, Ho Chi Minh City, Vietnam.

C Vietnam Academy of Science and Technology (VAST), Institute of Tropical Biology, 85 Tran Quoc Toan Street, District 3, Ho Chi Minh City, Vietnam.

D Ho Chi Minh City University of Technology, Vietnam National University – Ho Chi Minh City, 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam.

E Corresponding author. Email: manhhakg@yahoo.com.vn

Marine and Freshwater Research - https://doi.org/10.1071/MF16327
Submitted: 21 September 2016  Accepted: 12 March 2017   Published online: 22 May 2017

Abstract

An artificial neural network (ANN) model was used to predict the cyanobacteria bloom in the Dau Tieng Reservoir, Vietnam. Eight environmental parameters (pH, dissolved oxygen, temperature, total dissolved solids, total nitrogen (TN), total phosphorus, biochemical oxygen demand and chemical oxygen demand) were introduced as inputs, whereas the cell density of three cyanobacteria genera (Anabaena, Microcystis and Oscillatoria) with microcystin concentrations were introduced as outputs of the three-layer feed-forward back-propagation ANN. Eighty networks covering all combinations of four learning algorithms (Bayesian regularisation (BR), gradient descent with momentum and adaptive learning rate, Levenberg–Mardquart, scaled conjugate gradient) with two transfer functions (tansig, logsig) and 10 numbers of hidden neurons (6–16) were trained and validated to find the best configuration fitting the observed data. The result is a network using the BR learning algorithm, tansig transfer function and nine neurons in the hidden layer, which shows satisfactory predictions with the low values of error (root mean square error = 0.108) and high correlation coefficient values (R = 0.904) between experimental and predicted values. Sensitivity analysis on the developed ANN indicated that TN and temperature had the most positive and negative effects respectively on microcystin concentrations. These results indicate that ANN modelling can effectively predict the behaviour of the cyanobacteria bloom process.

Additional keywords: harmful algal blooms, microcystins, sensitivity analysis.


References

Amé, M. V., and Wunderlin, D. A. (2005). Effects of iron, ammonium and temperature on microcystin content by a natural concentrated Microcystis aeruginosa population. Water, Air, and Soil Pollution 168, 235–248.
Effects of iron, ammonium and temperature on microcystin content by a natural concentrated Microcystis aeruginosa population.CrossRef | open url image1

Anupam, K., Dutta, S., Bhattacharjee, C., and Datta, S. (2016). Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon. Desalination and Water Treatment 57, 3632–3641.
Artificial neural network modelling for removal of chromium (VI) from wastewater using physisorption onto powdered activated carbon.CrossRef | 1:CAS:528:DC%2BC2cXitVClurvN&md5=5305a352355d044f37200ffd29aadbd1CAS | open url image1

Azizi, S. N., Hosseinzadeh Colagar, A., and Hafeziyan, S. M. (2012). Removal of CdII from aquatic system using Oscillatoria sp. biosorbent. The Scientific World Journal 2012, 347053.
Removal of CdII from aquatic system using Oscillatoria sp. biosorbent.CrossRef | open url image1

Bhatti, M. S., Kapoor, D., Kalia, R. K., Reddy, A. S., and Thukral, A. K. (2011). RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: multi objective optimization using genetic algorithm approach. Desalination 274, 74–80.
RSM and ANN modeling for electrocoagulation of copper from simulated wastewater: multi objective optimization using genetic algorithm approach.CrossRef | 1:CAS:528:DC%2BC3MXltl2ju74%3D&md5=19e3f692ab0e5bbe50929de8a04e3602CAS | open url image1

Bui, H. M., Perng, Y. S., and Duong, H. G. T. (2016). The use of artificial neural network for modeling coagulation of reactive dye wastewater using Cassia fistula Linn. (CF) gum. Journal of Environmental Science and Management 19, 1–8. open url image1

Cerco, C. F., Noel, M. R., and Tillman, D. H. (2004). A practical application of Droop nutrient kinetics (WR 1883). Water Research 38, 4446–4454.
A practical application of Droop nutrient kinetics (WR 1883).CrossRef | 1:CAS:528:DC%2BD2cXhtVSiurzJ&md5=1d3dd37043082edc419f83ea33f20ce8CAS | open url image1

Chau, K. W., and Wu, C. L. (2010). A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. Journal of Hydroinformatics 12, 458–473.
A hybrid model coupled with singular spectrum analysis for daily rainfall prediction.CrossRef | open url image1

Chorus, I., and Bartram, J. (1999). ‘Toxic Cyanobacteria in Water: A Guide to their Public Health Consequences, Monitoring and Management.’ (Spon Press: London, UK.)

Clesceri, L. S., Greenberg, A. E., and Eaton, A. D. (1998). ‘Standard Methods for the Examination of Water and Wastewater’, 20th edn. (American Public Health Association: Washington, DC, USA.)

Coppola, E. A., Jacinto, A. B., Atherholt, T., Poulton, M., Pasquarello, L., Szidarvoszky, F., and Lohbauer, S. (2013). Using artificial neural networks for forecasting algae counts in a surface water system. Korean Journal of Ecology and Environment 46, 1–9.
Using artificial neural networks for forecasting algae counts in a surface water system.CrossRef | open url image1

Daneshvar, N., Khataee, A. R., and Djafarzadeh, N. (2006). The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process. Journal of Hazardous Materials 137, 1788–1795.
The use of artificial neural networks (ANN) for modeling of decolorization of textile dye solution containing C. I. Basic Yellow 28 by electrocoagulation process.CrossRef | 1:CAS:528:DC%2BD28XhtVWrtrfP&md5=27ce8c05b91748c4eaaef9f1cda2aa0fCAS | open url image1

Dolman, A. M., Rücker, J., Pick, F. R., Fastner, J., Rohrlack, T., Mischke, U., and Wiedner, C. (2012). Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS One 7, e38757.
Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus.CrossRef | 1:CAS:528:DC%2BC38XptFaltLk%3D&md5=d375659a7ce5b75c9408a898f74520abCAS | open url image1

Dong, X., Zeng, S., Bai, F., Li, D., and He, M. (2016). Extracellular microcystin prediction based on toxigenic Microcystis detection in a eutrophic lake. Scientific Reports 6, 20886.
Extracellular microcystin prediction based on toxigenic Microcystis detection in a eutrophic lake.CrossRef | 1:CAS:528:DC%2BC28XisFKhtLs%3D&md5=f8873b7ef1e7b38f4ae35626a4710254CAS | open url image1

Harris, T. D., Wilhelm, F. M., Graham, J. L., and Loftin, K. A. (2014). Experimental manipulation of TN : TP ratios suppress cyanobacterial biovolume and microcystin concentration in large-scale in situ mesocosms. Lake and Reservoir Management 30, 72–83.
Experimental manipulation of TN : TP ratios suppress cyanobacterial biovolume and microcystin concentration in large-scale in situ mesocosms.CrossRef | 1:CAS:528:DC%2BC2cXjslahsLk%3D&md5=f66c5251f70bd0b4d7b2afac6ac9cffbCAS | open url image1

Holmberg, M., Forsius, M., Starr, M., and Huttunen, M. (2006). An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change. Ecological Modelling 195, 51–60.
An application of artificial neural networks to carbon, nitrogen and phosphorus concentrations in three boreal streams and impacts of climate change.CrossRef | open url image1

Joung, S.-H., Oh, H.-M., Ko, S.-R., and Ahn, C.-Y. (2011). Correlations between environmental factors and toxic and non-toxic Microcystis dynamics during bloom in Daechung Reservoir, Korea. Harmful Algae 10, 188–193.
Correlations between environmental factors and toxic and non-toxic Microcystis dynamics during bloom in Daechung Reservoir, Korea.CrossRef | open url image1

Kundu, P., Debsarkar, A., and Mukherjee, S. (2013). Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor. Advances in Artificial Neural Systems 2013, 268064.
Artificial neural network modeling for biological removal of organic carbon and nitrogen from slaughterhouse wastewater in a sequencing batch reactor.CrossRef | open url image1

Kuo, J.-T., Hsieh, M.-H., Lung, W.-S., and She, N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling 200, 171–177.
Using artificial neural network for reservoir eutrophication prediction.CrossRef | open url image1

Lek, S., Guiresse, M., and Giraudel, J.-L. (1999). Predicting stream nitrogen concentration from watershed features using neural networks. Water Research 33, 3469–3478.
Predicting stream nitrogen concentration from watershed features using neural networks.CrossRef | 1:CAS:528:DyaK1MXmsleqtb8%3D&md5=44b2f67689916c63ac6e3a25f553d0d5CAS | open url image1

Luo, W., Chen, H., Lei, A., Lu, J., and Hu, Z. (2014). Estimating Cyanobacteria community dynamics and its relationship with environmental factors. International Journal of Environmental Research and Public Health 11, 1141–1160.
Estimating Cyanobacteria community dynamics and its relationship with environmental factors.CrossRef | open url image1

Machón, I., López, H., Rodríguez-Iglesias, J., Marañón, E., and Vázquez, I. (2007). Simulation of a coke wastewater nitrification process using a feed-forward neuronal net. Environmental Modelling & Software 22, 1382–1387.
Simulation of a coke wastewater nitrification process using a feed-forward neuronal net.CrossRef | open url image1

Maier, H. R., and Dandy, G. C. (2000). Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software 15, 101–124.
Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications.CrossRef | open url image1

Merdun, H., and Cinar, O. (2010). Artificial neural network and regression techniques in modelling surface water quality. Environment Protection Engineering 36, 95–109.
| 1:CAS:528:DC%2BC3cXht1WgtLvN&md5=355fdc3326bd9e5d42b55095b155e1b0CAS | open url image1

Merel, S., Walker, D., Chicana, R., Snyder, S., Baurès, E., and Thomas, O. (2013). State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environment International 59, 303–327.
State of knowledge and concerns on cyanobacterial blooms and cyanotoxins.CrossRef | 1:CAS:528:DC%2BC3sXhtl2qtb%2FM&md5=35c3703898cf6f472617d8969bbea76eCAS | open url image1

Monchamp, M.-E., Pick, F. R., Beisner, B. E., and Maranger, R. (2014). Nitrogen forms influence microcystin concentration and composition via changes in cyanobacterial community structure. PLoS One 9, e85573.
Nitrogen forms influence microcystin concentration and composition via changes in cyanobacterial community structure.CrossRef | open url image1

Mowe, M. A. D., Mitrovic, S. M., Lim, R. P., Furey, A., and Yeo, D. C. J. (2015). Tropical cyanobacterial blooms: a review of prevalence, problem taxa, toxins and influencing environmental factors. Journal of Limnology 74, 205–224. open url image1

Nasr, M. S., Moustafa, M. A. E., Seif, H. A. E., and El Kobrosy, G. (2012). Application of artificial neural network (ANN) for the prediction of El-Agamy wastewater treatment plant performance – Egypt. Alexandria Engineering Journal 51, 37–43.
Application of artificial neural network (ANN) for the prediction of El-Agamy wastewater treatment plant performance – Egypt.CrossRef | 1:CAS:528:DC%2BC38XhvVOis7zM&md5=bcc0254f9638d5f4f34fb0fc47997dd2CAS | open url image1

Ou, H.-S., Wei, C.-H., Wu, H.-Z., Mo, C.-H., and He, B.-Y. (2015). Sequential dynamic artificial neural network modeling of a full-scale coking wastewater treatment plant with fluidized bed reactors. Environmental Science and Pollution Research International 22, 15910–15919.
Sequential dynamic artificial neural network modeling of a full-scale coking wastewater treatment plant with fluidized bed reactors.CrossRef | 1:CAS:528:DC%2BC2MXhtVKqtrrF&md5=a2210ee9c75812afee7b6504bcafc554CAS | open url image1

Paerl, H. W., and Paul, V. J. (2012). Climate change: links to global expansion of harmful cyanobacteria. Water Research 46, 1349–1363.
Climate change: links to global expansion of harmful cyanobacteria.CrossRef | 1:CAS:528:DC%2BC38XitFWntrc%3D&md5=0cbe510ae3968017d554ea22b5450268CAS | open url image1

Pai, T. Y., Yang, P. Y., Wang, S. C., Lo, M. H., Chiang, C. F., Kuo, J. L., Chu, H. H., Su, H. C., Yu, L. F., Hu, H. C., and Chang, Y. H. (2011). Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Applied Mathematical Modelling 35, 3674–3684.
Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality.CrossRef | open url image1

Pakravan, P., Akhbari, A., Moradi, H., Azandaryani, A. H., Mansouri, A. M., and Safari, M. (2015). Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube. Applied Petrochemical Research 5, 47–59.
Process modeling and evaluation of petroleum refinery wastewater treatment through response surface methodology and artificial neural network in a photocatalytic reactor using poly ethyleneimine (PEI)/titania (TiO2) multilayer film on quartz tube.CrossRef | 1:CAS:528:DC%2BC2cXhsVSis7vE&md5=1ddaa3a97ae31c09b5aa3a90ed55ff7aCAS | open url image1

Pham, T.-L., Dao, T.-S., Shimizu, K., Lan-Chi, D.-H., and Utsumi, M. (2015). Isolation and characterization of microcystin-producing cyanobacteria from Dau Tieng Reservoir, Vietnam. Nova Hedwigia 101, 3–20.
Isolation and characterization of microcystin-producing cyanobacteria from Dau Tieng Reservoir, Vietnam.CrossRef | open url image1

Reynolds, C. S., and Irish, A. E. (1997). Modelling phytoplankton dynamics in lakes and reservoirs: the problem of in-situ growth rates. Hydrobiologia 349, 5–17.
Modelling phytoplankton dynamics in lakes and reservoirs: the problem of in-situ growth rates.CrossRef | 1:CAS:528:DyaK2sXnslOit7g%3D&md5=246057410be8a30dba43c49bed22a19aCAS | open url image1

Reynolds, C. S., Oliver, R. L., and Walsby, A. E. (1987). Cyanobacterial dominance: the role of buoyancy regulation in dynamic lake environments. New Zealand Journal of Marine and Freshwater Research 21, 379–390.
Cyanobacterial dominance: the role of buoyancy regulation in dynamic lake environments.CrossRef | open url image1

Reynolds, C. S., Irish, A. E., and Elliott, J. A. (2001). The ecological basis for simulating phytoplankton responses to environmental change (PROTECH). Ecological Modelling 140, 271–291.
The ecological basis for simulating phytoplankton responses to environmental change (PROTECH).CrossRef | 1:CAS:528:DC%2BD3MXkt1Kgtro%3D&md5=004bfdffb60182bb79fb6e3e57b23124CAS | open url image1

Sahoo, G. B., Ray, C., Mehnert, E., and Keefer, D. A. (2006). Application of artificial neural networks to assess pesticide contamination in shallow groundwater. The Science of the Total Environment 367, 234–251.
Application of artificial neural networks to assess pesticide contamination in shallow groundwater.CrossRef | 1:CAS:528:DC%2BD28Xnt1yhtL0%3D&md5=4540f3e3986f3b5b9bdbc6bb2378ae22CAS | open url image1

Sorayya, M. (2012). Supervised and unsupervised artificial neural networks for analysis of diatom abundance in Tropical Putrajaya Lake, Malaysia. Sains Malaysiana 41, 939–947.
| 1:CAS:528:DC%2BC38XhsVKjsrzE&md5=126253aea5ff96ae2eedbc4d77409d02CAS | open url image1

Su, M., Yu, J., Zhang, J., Chen, H., An, W., Vogt, R. D., Andersen, T., Jia, D., Wang, J., and Yang, M. (2015). MIB-producing cyanobacteria (Planktothrix sp.) in a drinking water reservoir: distribution and odor producing potential. Water Research 68, 444–453.
MIB-producing cyanobacteria (Planktothrix sp.) in a drinking water reservoir: distribution and odor producing potential.CrossRef | 1:CAS:528:DC%2BC2cXhslOis7%2FF&md5=ac6fcd186d440ad5e753959cfdfcc9ecCAS | open url image1

Te, S. H., and Gin, K. Y.-H. (2011). The dynamics of cyanobacteria and microcystin production in a tropical reservoir of Singapore. Harmful Algae 10, 319–329.
The dynamics of cyanobacteria and microcystin production in a tropical reservoir of Singapore.CrossRef | 1:CAS:528:DC%2BC3MXhslOlu7k%3D&md5=1b0fb43e854e1339ca29c4fca1b28851CAS | open url image1

van der Westhuizen, J., and Eloff, J. N. (1985). Effect of temperature and light on the toxicity and growth of the blue–green alga Microcystis aeruginosa (UV-006). Planta 163, 55–59.
Effect of temperature and light on the toxicity and growth of the blue–green alga Microcystis aeruginosa (UV-006).CrossRef | 1:STN:280:DC%2BC2c7osVWhsw%3D%3D&md5=a7c7d8ad2dbada19ce3788445f48a96fCAS | open url image1

Vasas, G., Farkas, O., Borics, G., Felföldi, T., Sramkó, G., Batta, G., Bácsi, I., and Gonda, S. (2013). Appearance of Planktothrix rubescens Bloom with [D-Asp(3), Mdha(7)]MC-RR in gravel pit pond of a shallow lake-dominated area. Toxins 5, 2434–2455.
Appearance of Planktothrix rubescens Bloom with [D-Asp(3), Mdha(7)]MC-RR in gravel pit pond of a shallow lake-dominated area.CrossRef | 1:CAS:528:DC%2BC2cXntVOgtLY%3D&md5=bf1027bf9cff7159b116863e79276d15CAS | open url image1

Wang, X., Ji, Y., and Li, X. (2011). Research on the prediction of water treatment plant coagulant dosage based on feed-forward artificial neutral network. In ‘2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)’, 16–18 April 2011, XianNing, China. (Eds Z. Xia and L. Zhang.) pp. 1615–1617. (IEEE, Red Hook, NY, USA.)

Wei, B., Sugiura, N., and Maekawa, T. (2001). Use of artificial neural network in the prediction of algal blooms. Water Research 35, 2022–2028.
Use of artificial neural network in the prediction of algal blooms.CrossRef | 1:CAS:528:DC%2BD3MXisFCgtrs%3D&md5=ffff18063c9292ffe03edca1981fa498CAS | open url image1

Yabunaka, K.-i., Hosomi, M., and Murakami, A. (1997). Novel application of a back-propagation artificial neural network model formulated to predict algal bloom. Water Science and Technology 36, 89–97.
Novel application of a back-propagation artificial neural network model formulated to predict algal bloom.CrossRef | 1:CAS:528:DyaK1cXosVGg&md5=795f8c19ecc8d3d8084cb6ddf867044cCAS | open url image1

Ye, W., Liu, X., Tan, J., Li, D., and Yang, H. (2009). Diversity and dynamics of microcystin-producing cyanobacteria in China’s third largest lake, Lake Taihu. Harmful Algae 8, 637–644.
Diversity and dynamics of microcystin-producing cyanobacteria in China’s third largest lake, Lake Taihu.CrossRef | 1:CAS:528:DC%2BD1MXotlOgu7c%3D&md5=0b3b0936659e7709d01ed728d00c14c6CAS | open url image1



Export Citation

View Altmetrics