Freshwater Algal Bloom Prediction by Support Vector Machine in Macau Storage Reservoirs

Zhengchao Xie,Inchio Lou,Wai Kin Ung,Kai Meng Mok

doi:10.1155/2012/397473

Zhengchao Xie, Inchio Lou + Show 2 more

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https://doi.org/10.1155/2012/397473

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Abstract

Understanding and predicting dynamic change of algae population in freshwater reservoirs is particularly important, as algae-releasing cyanotoxins are carcinogens that would affect the health of public. However, the high complex nonlinearity of water variables and their interactions makes it difficult to model the growth of algae species. Recently, support vector machine (SVM) was reported to have advantages of only requiring a small amount of samples, high degree of prediction accuracy, and long prediction period to solve the nonlinear problems. In this study, the SVM-based prediction and forecast models for phytoplankton abundance in Macau Storage Reservoir (MSR) are proposed, in which the water parameters of pH, SiO2, alkalinity, bicarbonate(HCO3 -), dissolved oxygen (DO), total nitrogen (TN), UV254, turbidity, conductivity, nitrate, total nitrogen (TN), orthophosphate(PO4 3−), total phosphorus (TP), suspended solid (SS) and total organic carbon (TOC) selected from the correlation analysis of the 23 monthly water variables were included, with 8-year (2001–2008) data for training and the most recent 3 years (2009–2011) for testing. The modeling results showed that the prediction and forecast powers were estimated as approximately 0.76 and 0.86, respectively, showing that the SVM is an effective new way that can be used for monitoring algal bloom in drinking water storage reservoir.

Highlights

Freshwater algal bloom is one of water pollution problems that occurs in eutrophic lakes or reservoirs due to the presence of excessive nutrients
It was noted that the parameters selected in forecast models are different from those in the prediction models, as the water parameters in previous data past record were used in the correlation analysis
In the forecast models of SVM, phytoplankton abundance t is a function of water parameter t-1, water parameter t-2, and water parameters t-3, where t-1, t-2, and t-3 represent the 1 month, 2 months, and 3 months prior to time t

Summary

Introduction

Freshwater algal bloom is one of water pollution problems that occurs in eutrophic lakes or reservoirs due to the presence of excessive nutrients. With the development of artificial intelligence models, artificial neural network ANN such as backpropagation BP was applied to predict the algal bloom by assessing the eutrophication and simulating the chlorophyll-a concentration. ANN is a well-suited method with self-adaptability, selforganization, and error tolerance, which is better than PCR for nonlinear simulation. This method has such limitations as requirement of a great amount of training data, difficulty in tuning the structure parameter that is mainly based on experience, and its “black box” nature that makes it difficult to understand and interpret the data 2, 3

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Conclusion