Use of artificial neural network in the prediction of algal blooms

doi:10.1016/S0043-1354(00)00464-4

Water Research

Volume 35, Issue 8, June 2001, Pages 2022-2028

https://doi.org/10.1016/S0043-1354(00)00464-4 Get rights and content

Abstract

A model to quantify the interactions between abiotic factors and algal genera in Lake Kasumigaura, Japan was developed using artificial neural network technology. Results showed that the timing and magnitude of algal blooms of Microcystis, Phormidium and Synedra in Lake Kasumigaura could be successfully predicted. As for the newly occurring dominant Oscillatoria, results were not satisfactory. The evaluation of the importance of factors showed that Microcystis, Phormidium, Oscillatoria and Synedra were alkalophilic. The algal proliferation for Microcystis, Oscillatoria and Synedra decrease due to the increase in total nitrogen, while the growth of Phormidium is enhanced with more nitrogen. In addition, the algal density is affected by zooplankton grazing but with the exception of Phormidium due to it being poor food source. Algal responses to the orthogonal combinations of the external environmental factors, chemical oxygen demand, pH, total nitrogen and total phosphorus at three levels were modeled. Various combinations of environmental factors enhance the proliferation of some algae while other combinations inhibit bloom formation.

Introduction

The vast proliferation of cyanobacteria in lakes and reservoirs has become an emergent aquatic environmental issue owing to fish mortalities caused by oxygen-depletion, toxins such as microcystin and anatoxin, the occurrence of an unpalatable taste and odor, as well as the loss of recreational resources (Pinckney et al., 1997; Inamori et al., 1998; Sugiura et al., 1998). The development of cyanobacteria and green algae blooms are a common phenomenon in eutrophicated waters (Smith, 1990). Research on preventive technologies and monitoring methods of algal bloom have accordingly received worldwide attention.

To be able to control algal blooms, it is necessary to be able to determine the key factors governing the algal dynamics and to establish an algal response model which can effectively simulate the timing and magnitude of algal blooms. Recently, artificial neural network (ANN) technology has been applied in the prediction of algal blooms (Recknagel et al., 1997; Yabunaka et al., 1997), and the succession of several dominant cyanobacteria (Recknagel, 1997). However, the effects of different factor combinations have not been assessed in detail. Observations have shown that some selected factors in the input layer lead to “noise” affecting the output results. Therefore, an evaluation of the factors affecting the algal growth is necessary before creating an artificial neural network. Further, it is important for successful lake management to find the optimal combinations of these factors based on the availability of data and measures to regulate pollution inputs into the system.

In this study, the most commonly used computational algorithm, back-propagation, was used in ANN model to determine the nonlinear relationships between the water quality factors and the dominant algal genera. Relative importance of factors affecting algal growth was evaluated by sensitivity analysis. Finally, the optimized combinations were determined by observing the change of algal densities using the factorial orthogonal design.

Section snippets

Study site and water quality data

Lake Kasumigaura is the second largest lake in Japan with an area of 22,000 ha and water mean depth of 4.0 m. Mean hydrological retention time is 200 days. From 1982 to 1996, near the center of lake, the annual average water temperature and rainfall was 16.0°C and 1202 mm, respectively. Water quality data from 1982 to 1996 in Kakezaki sampling station used in this study were provided by the Enterprise Bureau of Ibaraki Prefecture (Annual Reports of Water Quality from 1982 to 1996).

Identification of factors affecting the algal growth

Factors affecting the growth of algae in lake ecosystems are multidimensional, including physical factors (water temperature, light radiation, and the conditioning of water environment), stimulatory or inhibitory chemicals (nutrient loading, aquatic humic substances) as well as biotic interactions (species-species competition, predation). Among these factors, temperature, light irradiance, concentration and composition of nutrients, the conditioning of water, and the rate of cell division were

Artificial neural network technology

ANN is an appropriate technique to develop models because of its abilities to assign significance to the input parameters and map the inputs to outputs when the relationships between parameters are unknown. Especially, the backpropagation learning technique has been applied successfully in nonlinear complex systems, as it can arbitrarily approximate functions by using the gradient descent algorithm or faster algorithm (Hagan et al., 1996). Recently, ANN has been widely used in water resource

Prediction of the timing and magnitude of algal blooms

MSE decreased rapidly and remained around 0. 0195 for Microcystis, 0.0216 for Oscillatoria, 0.0265 for Phormidium and 0.0262 for Synedra after 1000 epochs. The post-processed results for the four representative algae using the developed network are shown in Table 1. The trained values were significantly correlated to the measured data (p<0.001) for Microcystis, Oscillatoria, Phormidium and Synedra, viewing from the correlation coefficient and trained data points.

Further, the trained networks

Conclusions

An ANN model to simulate the algal proliferation for the four dominant genera, Microcystis, Oscillatoria, Phormidium and Synedra was created. The factors affecting the algal growth were selected to be input parameters based on previous studies.

The proliferation of algal blooms of Microcystis, Phormidium and Synedra in Lake Kasumigaura was successfully predicted and validated using neural network technology. Comparatively, the prediction of Oscillatoria blooms, a new dominant phytoplankton since

Acknowledgements

The study was supported by the Scientific and Technology Promotion Society of Ibaraki Prefecture, Japan. We are also very grateful for water quality monitoring data provided by the Enterprise Bureau of Ibaraki Prefecture.

References (25)

F Recknagel et al.
Artificial neural network approach for modeling and prediction of algal blooms
Ecol. Modell.
(1997)
N Sugiura et al.
Significance of attached cyanobacteria relevant to the occurrence of musty odor in Lake Kasumigaura
Wat. Res.
(1998)
K.-I Yabunaka et al.
Novel application of a back-propagation artificial neural network model formulated to predict algal bloom
Wat. Sci. Tech.
(1997)
P Bougis
Marine Plankton Ecology
(1976)
T.A Clair et al.
Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers
Water Resour. Res.
(1998)
Enterprise Bureau of Ibaraki Prefecture, Japan. (1982–1996) Annual Reports of Water Quality of Enterprise Bureau, Nos....
A.J.D Ferguson et al.
Structure and dynamics of zooplankton communities maintained in closed systems, with special reference to the algal food supply
J. Plankton Res.
(1982)
N Fujimoto et al.
Nutrient-limited growth of Microcystis aeruginosa and Phormidium tenue and competition under various NP supply ratios and temperatures
Limnol. Oceanogr.
(1997)
M.T Hagan et al.
Neural network design
(1996)
G.P Harris
Phytoplankton EcologyStructure, Function and Fluctuation
(1986)

K.E Havens

Particulate light attenuation in a large subtropical lake

Can. J. Fish. Aquat. Sci.

(1995)

A Imai et al.

Effects of aquatic humic substances on the growth the cyanobacterium Microcystis aeruginosa

Japan. J. Water Environ. Sci.

(1999)

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Water Research

Abstract

Introduction

Section snippets

Study site and water quality data

Identification of factors affecting the algal growth

Artificial neural network technology

Prediction of the timing and magnitude of algal blooms

Conclusions

Acknowledgements

References (25)

Artificial neural network approach for modeling and prediction of algal blooms

Ecol. Modell.

Significance of attached cyanobacteria relevant to the occurrence of musty odor in Lake Kasumigaura

Wat. Res.

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

Wat. Sci. Tech.

Marine Plankton Ecology

Using neural networks to assess the influence of changing seasonal climates in modifying discharge, dissolved organic carbon, and nitrogen export in eastern Canadian rivers

Water Resour. Res.

Structure and dynamics of zooplankton communities maintained in closed systems, with special reference to the algal food supply

J. Plankton Res.

Nutrient-limited growth of Microcystis aeruginosa and Phormidium tenue and competition under various NP supply ratios and temperatures

Limnol. Oceanogr.

Neural network design

Phytoplankton EcologyStructure, Function and Fluctuation

Particulate light attenuation in a large subtropical lake

Can. J. Fish. Aquat. Sci.

Effects of aquatic humic substances on the growth the cyanobacterium Microcystis aeruginosa

Japan. J. Water Environ. Sci.

Cited by (121)

Assessing physical and biological lake oxygen indicators using simulated environmental variables and machine learning algorithms

Prediction of Aureococcus anophageffens using machine learning and deep learning

Optimizing biogas production from palm oil mill effluent utilizing integrated machine learning and response surface methodology framework

High-resolution satellite observations reveal extensive algal blooms in both small and large lakes in China

Characterizing ocean surface contamination: Composition, film thickness, and rheology

Remote sensing to detect harmful algal blooms in inland waterbodies