A predictive model of chlorophyll a in western lake erie based on artificial neural network

Abstract

The reoccurrence of algal blooms in western Lake Erie (WLE) since the mid‐1990s, under increased system stress from climate change and excessive nutrients, has shown the need for developing management tools to predict water quality. In this study, process‐based model GLM‐AED (General Lake Model‐Aquatic Ecosystem Dynamics) and statistical model ANN (artificial neural network) were developed with meteorological forcing derived from surface buoys, airports, and land‐based stations and historical monitoring nutrients, to predict water quality in WLE from 2002 to 2015. GLM‐AED was calibrated with observed water temperature and chlorophyll a (Chl‐a) from 2002 to 2015. For ANN, during the training period (2002–2010), the inputs included meteorological forcing and nutrient concentrations, and the target was Chl‐a simulated by calibrated GLM‐AED due to the lack of continuously daily measured Chl‐a concentrations. During the testing period (2011–2015), the predicted Chl‐a concentrations were compared with the observations. The results showed that the ANN model has higher accuracy with lower Chl‐a RMSE and MAE values than GLM‐AED during 2011 and 2015. Lastly, we applied the established ANN model to predict the future 10‐year water quality of WLE, which showed that the probability of adverse health effects would be moderate, so more intense water resources management should be implemented.