Comparing artificial intelligence techniques for chlorophyll-a prediction in US lakes

Chlorophyll-a (CHLA) is a key indicator to represent eutrophication status in lakes. In this study, CHLA, total phosphorus (TP), total nitrogen (TN), turbidity (TB), and Secchi depth (SD) collected by the United States Environmental Protection Agency for the National Lakes Assessment in the continental USA were analyzed. Statistical analysis showed that water quality variables in natural lakes have strong patterns of autocorrelations than man-made lakes, indicating the perturbation of anthropogenic stresses on man-made lake ecosystems. Meanwhile, adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean–clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC) were implemented to model CHLA in lakes, and modeling results were compared with the multilayer perceptron neural network models (MLPNN). Results showed that ANFIS_FC models outperformed other models for natural lakes, while for man-made lakes, MLPNN models performed the best. ANFIS_GP models have the lowest accuracies in general. The results indicated that ANFIS models can be screening tools for an overall estimation of CHLA levels of lakes in large scales, especially for natural lakes.