초록/요약

It is difficult to predict when, where, and how long algal blooms will occur in a water body. The objectives of this study were to determine the factors affecting algal bloom and predict chlorophyll-a (Chl-a) levels in the reservoir formed by damming a river using an artificial neural network (ANN). The automatic water quality monitoring data [ water temperature, pH, dissolved oxygen (DO), electric conductivity, total organic carbon (TOC), Chl-a, total nitrogen (T-N), and total phosphorus (T-P)], weather data (precipitation, temperature, insolation, and duration of sunshine) and hydrologic data (water level, discharges, and inflows) in the man-made Lake Juam during 2008-2010 were used to develop a model to predict Chl-a as an indirect measure of the abundance of algae. The ANN was trained using the collected data during 2008-2010 and the accuracy of the model was verified using the data collected in 2011. It was found that Chl-a concentration, TOC, pH and atmospheric and water temperatures were the most important parameters in predicting Chl-a concentrations. The Chl-a prediction was most influenced by the parameters showing the algal activities such as Chl-a, TOC and pH. Due to the relatively long hydraulic retention time of similar to 131 days, the inflow and outflow did not affect the prediction much. Likewise, atmospheric and water temperatures did not respond to the change of the Chl-a concentration due to the lake's relatively slow response to the temperature. Most importantly, T-N and T-P were not the major factors in predicting Chl-a levels at Lake Juam. The ANN trained with the time series data successfully predicted the Chl-a concentration and provided information regarding the principal factors affecting algal bloom at Lake Juam. (c) 2014 Elsevier Ltd. All rights reserved.