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 ∼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.
|Number of pages||10|
|Journal||Measurement: Journal of the International Measurement Confederation|
|State||Published - Jul 2014|
- Artificial neural network (ANN)
- Chlorophyll-a (Chl-a)
- Man-made lake
- Total organic carbon (TOC)