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Park, Yongeun,Pyo, JongCheol,Kwon, Yong Sung,Cha, YoonKyung,Lee, Hyuk,Kang, Taegu,Cho, Kyung Hwa Elsevier 2017 Water research Vol.126 No.-
<P><B>Abstract</B></P> <P>Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = −0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = −0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Factors affecting on cyanobacterial blooms were determined using two-year cell data. </LI> <LI> Hyperspectral images were used to describe the spatiotemporal variations in blooms. </LI> <LI> Effects of hydrodynamics on the spatiotemporal variations in blooms were assessed. </LI> <LI> Cyanobacterial biomass depended significantly on four environmental factors. </LI> <LI> Hydrodynamic parameters had a partial effect on the variations in the blooms. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Park, Yongeun,Ligaray, Mayzonee,Kim, Young Mo,Kim, Joon Ha,Cho, Kyung Hwa,Sthiannopkao, Suthipong Informa UK (Taylor Francis) 2016 Desalination and Water Treatment Vol.57 No.26
<P>Groundwater contamination with arsenic (As) is one of the major issues in the world, especially for Southeast Asian (SEA) countries where groundwater is the major drinking water source, especially in rural areas. Unfortunately, quantification of groundwater As contamination is another burden for those countries because it requires sophisticated equipment, expensive analysis, and well-trained technicians. Here, we collected approximately 350 groundwater samples from three different SEA countries, including Cambodia, Lao PDR, and Thailand, in an attempt to quantify total As concentrations and conventional water quality variables. After that, two machine learning models (i.e. artificial neural network (ANN) and support vector machine (SVM)) were applied to predict groundwater As contamination using conventional water quality parameters. Prior to modeling approaches, the pattern search algorithm in MATLAB software was used to optimize the ANN and SVM model parameters, attempting to find the best parameters set for modeling groundwater As concentrations. Overall, the SVM showed the superior prediction performance, giving higher Nash-Sutcliffe coefficients than ANN in both the training and validation periods. We hope that the model developed by this study could be a suitable quantification tool for groundwater As contamination in SEA countries.</P>