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 68(11) 2070-2080 https://doi.org/10.1071/MF16327
Submitted: 21 September 2016 Accepted: 12 March 2017 Published: 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.
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