Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom

Pawalee Srisuksomwong, Jeeraporn Pekkoh


Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxic cyanobacteria in Thailand. Difficulty prediction for planning protects Maekuang reservoirs, the artificial Neural Network (ANN) model is a powerful tool that can be used to machine learning and prediction by observation data. ANN is able to learn from previous data and has been used to predict the value in the future. ANN consists of three layers as input, hidden, and output layer. Water quality data is collected biweekly at Maekuang reservoir (1999-2000). Input data for training, including nutrients (ammonium, nitrate, and phosphorus), Secchi depth, BOD, temperature, conductivity, pH, and output data for testing as Chlorophyll a and M. aeruginosa cells. The model was evaluated using four performances, namely; mean squared error (MSE), root mean square error (RMSE), sum of square error (SSE), and percentage error. It was found that the model prediction agreed with experimental data. C01-C08 scenarios focused on M. aeruginosa bloom prediction, and ANN tested for prediction of Chlorophyll a bloom shown on M01-M09 scenarios. The findings showed, this model has been validated for prediction of Chlorophyll a and shows strong agreement for nitrate, Log cell, and Chlorophyll a. Results indicate that the ANN can be predicted eutrophication indicators during the summer season, and ANN has efficient for providing the new data set and predict the behavior of M. aeruginosa bloom process.


Artificial Neural Network Model; Eutrophication; Toxic Cyanobacteria; Microcystis Aeruginosa.


Sanseverino, Isabella, Diana Conduto, Luca Pozzoli, Srdan Dobricic, and Teresa Lettieri. "Algal bloom and its economic impact." European Commission, Joint Research Centre Institute for Environment and Sustainability (2016).

Ahn, Chi-Yong, Myung-Hwan Park, Seung-Hyun Joung, Hee-Sik Kim, Kam-Yong Jang, and Hee-Mock Oh. “Growth Inhibition of Cyanobacteria by Ultrasonic Radiation: Laboratory and Enclosure Studies.” Environmental Science & Technology 37, no. 13 (July 2003): 3031–3037. doi:10.1021/es034048z.

Rodgers, John H. Jr. “Algal toxins in pond aquaculture.” SRAC Publication 4605, (November 2008): 1-8.

Zimba, Paul V, and Casey C Grimm. “A Synoptic Survey of Musty/muddy Odor Metabolites and Microcystin Toxin Occurrence and Concentration in Southeastern USA Channel Catfish (Ictalurus Punctatus Ralfinesque) Production Ponds.” Aquaculture 218, no. 1–4 (March 2003): 81–87. doi:10.1016/s0044-8486(02)00519-7.

Smith, Juliette L., Greg L. Boyer, and Paul V. Zimba. “A Review of Cyanobacterial Odorous and Bioactive Metabolites: Impacts and Management Alternatives in Aquaculture.” Aquaculture 280, no. 1–4 (August 2008): 5–20. doi:10.1016/j.aquaculture.2008.05.007.

Falconer, Ian R. “Potential Impact on Human Health of Toxic Cyanobacteria.” Phycologia 35, no. sup6 (November 1996): 6–11. doi:10.2216/i0031-8884-35-6s-6.1.

Codd, G. A., C. J. Ward, and S. G. Bell. “Cyanobacterial Toxins: Occurrence, Modes of Action, Health Effects and Exposure Routes.” Applied Toxicology: Approaches Through Basic Science (1997): 399–410. doi:10.1007/978-3-642-60682-3_38.

Chorus, Ingrid, and Jamie Bartram, eds. “Toxic Cyanobacteria in Water” (1999). doi:10.4324/9780203478073.

Sultan, Abdullah. “New Artificial Neural Network Model for Predicting the TOC from Well Logs.” SPE Middle East Oil and Gas Show and Conference (2019). doi:10.2118/194716-ms.

Sinha, Ankita, and Atul Bhargav. “An Artificial Neural Network Model for Predicting Characteristic Input Parameters for Physics Based Modelling of Drying Process” (November 2, 2019). doi:10.31224/

Agwu, Okorie E., Julius U. Akpabio, and Adewale Dosunmu. “Artificial Neural Network Model for Predicting Drill Cuttings Settling Velocity.” Petroleum (December 2019). doi:10.1016/j.petlm.2019.12.003.

Zheng, Chunlei, and Rong Xu. “Predicting Cancer Origins with a DNA Methylation-Based Deep Neural Network Model” (November 29, 2019). doi:10.1101/860171.

Wei, Bin, Norio Sugiura, and Takaaki Maekawa. “Use of Artificial Neural Network in the Prediction of Algal Blooms.” Water Research 35, no. 8 (June 2001): 2022–2028. doi:10.1016/s0043-1354(00)00464-4.

Lee, Joseph H.W., Yan Huang, Mike Dickman, and A.W. Jayawardena. “Neural Network Modelling of Coastal Algal Blooms.” Ecological Modelling 159, no. 2–3 (January 2003): 179–201. doi:10.1016/s0304-3800(02)00281-8.

Kuo, Jan-Tai, Ming-Han Hsieh, Wu-Seng Lung, and Nian She. “Using Artificial Neural Network for Reservoir Eutrophication Prediction.” Ecological Modelling 200, no. 1–2 (January 2007): 171–177. doi:10.1016/j.ecolmodel.2006.06.018.

Raee, Mohammad, and Mahsa Jahangiri-Rad. “Artificial Neural Network Approaches to the Prediction of Eutrophication and Algal Blooms in Aras Dam Iran.” Iranian Journal of Health Science 3, no.1 (February 2015): 25-32. doi:10.7508/ijhs.2015.01.004.

Tian, Wenchong, Zhenliang Liao, and Jin Zhang. “An Optimization of Artificial Neural Network Model for Predicting Chlorophyll Dynamics.” Ecological Modelling 364 (November 2017): 42–52. doi:10.1016/j.ecolmodel.2017.09.013.

Lee, Joseph H.W., Yan Huang, Mike Dickman, and A.W. Jayawardena. “Neural Network Modelling of Coastal Algal Blooms.” Ecological Modelling 159, no. 2–3 (January 2003): 179–201. doi:10.1016/s0304-3800(02)00281-8.

Wang, Li, Xiaoyi Wang, Xuebo Jin, Jiping Xu, Huiyan Zhang, Jiabin Yu, Qian Sun, Chong Gao, and Lingbin Wang. “Analysis of Algae Growth Mechanism and Water Bloom Prediction Under the Effect of Multi-Affecting Factor.” Saudi Journal of Biological Sciences 24, no. 3 (March 2017): 556–562. doi:10.1016/j.sjbs.2017.01.026.

Bui, Manh-Ha, Thanh-Luu Pham, and Thanh-Son Dao. “Prediction of Cyanobacterial Blooms in the Dau Tieng Reservoir Using an Artificial Neural Network.” Marine and Freshwater Research 68, no. 11 (2017): 2070. doi:10.1071/mf16327.

Luo, Wenhuai, Huirong Chen, Anping Lei, Jun Lu, and Zhangli Hu. “Estimating Cyanobacteria Community Dynamics and Its Relationship with Environmental Factors.” International Journal of Environmental Research and Public Health 11, no. 1 (January 20, 2014): 1141–1160. doi:10.3390/ijerph110101141.

Mowe, Maxine A.D., Simon M. Mitrovic, Richard P. Lim, Ambrose Furey, and Darren C.J. Yeo. “Tropical Cyanobacterial Blooms: a Review of Prevalence, Problem Taxa, Toxins and Influencing Environmental Factors.” Journal of Limnology 73, no. AoP (December 30, 2014). doi:10.4081/jlimnol.2014.1005.

Full Text: PDF

DOI: 10.28991/esj-2020-01217


  • There are currently no refbacks.

Copyright (c) 2020 Pawalee Srisuksomwong, Jeeraporn Pekkoh