Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China

Abstract

Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.

A systematic literature review of forecasting and predictive models for cyanobacteria blooms in freshwater lakes

“…pH was often identified as a secondary positive predictor for CyanoHABs. Many cyanobacteria can survive in high-pH environments due to carbon concentrating mechanisms (Price et al, 2008) and tend to dominate eukaryotic algae in alkaline waters (Shapiro, 1987) such as observed by Recknagel et al (2006) in Lake Kasumigaura, Japan and by Liu et al (2015) in Yuqiao reservoir, China. The micronutrient silica was also identified as a secondary predictor in some publications but varied according to the species.…”