Predicting periphyton biomass is essential for controlling algal biomass in regulated rivers; however, accurate modeling remains a challenge. Many traditional models have been developed to predict periphyton biomass under different conditions, but they are only useful under specific conditions and cannot accurately model the wide range of factors that co-control the periphyton biomass. This prompts us to look at alternate systems, including artificial intelligence methods that have advantages in dealing with non-linear relationships in a complex system. In this study, we calculated the periphyton biomass based on three well-established models, using field data from the Ohyama River in Japan. The field data indicates that the average chlorophyll a concentration was 21.04 μg/cm2, which is above the ‘nuisance’ threshold. Among the three models, the performance of the least squares-support vector machine (LS-SVM) model was found to be superior to those of the artificial neural network (ANN) and multiple linear regression (MLR) models. The relationships between water temperature, light intensity, TN, TP, discharge, and chlorophyll a were derived based on the LS-SVM model. Two scenarios for flushing flow were identified (i.e., for ‘degraded’ and ‘common’ conditions) and optimal flow rates of ∼10 m3/s and 9 m3/s, respectively, were recorded. The findings can improve river management, optimize dam operations, and reduce excess periphyton growth in flow-reduced downstream reaches of rivers.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering